NAMIC Wiki - User contributions [en]

2017 Winter Project Week/2017 NVIDIA Demo Contest

2017-01-13T16:55:01Z

Clisle: /* Background and References */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

*Abdul Halabi, NVIDIA
*Abel Brown, NVIDIA

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* To identify a Deep Learning that is appropriate for adding to NVIDIA's demo suite
|

* Observe the projects in progress here, and award a TitanX card on Friday morning
|

|}

==Background and References==

Links to competition entries:
* Prostate Gland Segmentation with Fully Convolutional Neural Networks: (Alireza Mehrtash, Mehran Pesteie, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
** Youtube Link: https://youtu.be/5lvY0cZ4qfk
* [[2017 Winter Project Week/Knee Cartilage Segmentation | Knee Cartilage Segmentation]] (Hans Meine)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Paolo Zaffino, Ziyang Wang, Guillaume Pernelle, Tina Kapur)
*[[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)

2017 Winter Project Week/2017 NVIDIA Demo Contest

2017-01-13T16:53:45Z

Clisle: /* Background and References */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

*Abdul Halabi, NVIDIA
*Abel Brown, NVIDIA

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* To identify a Deep Learning that is appropriate for adding to NVIDIA's demo suite
|

* Observe the projects in progress here, and award a TitanX card on Friday morning
|

|}

==Background and References==

Links to competition entries:
* Prostate Gland Segmentation with Fully Convolutional Neural Networks: (Alireza Mehrtash, Mehran Pesteie, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
** Youtube Link: https://youtu.be/5lvY0cZ4qfk
* [[2017 Winter Project Week/Knee Cartilage Segmentation | Knee Cartilage Segmentation]] (Hans Meine)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Paolo Zaffino, Ziyang Wang, Guillaume Pernelle, Tina Kapur)
#[[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:35:32Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
KVis-deep-learning-project.pdf | Presentation Slides on the project
KVis-deep-learning-video.mov | Project Demonstration Video

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:35:05Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
pdf:KVis-deep-learning-project.pdf | Presentation Slides on the project
Media:KVis-deep-learning-video.mov | Project Demonstration Video

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:34:52Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
pdf:KVis-deep-learning-project.pdf | Presentation Slides on the project
[Media:KVis-deep-learning-video.mov | Project Demonstration Video]

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:34:36Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
pdf:KVis-deep-learning-project.pdf | Presentation Slides on the project
[[Media:KVis-deep-learning-video.mov | Project Demonstration Video]]

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:34:09Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
pdf:KVis-deep-learning-project.pdf | Presentation Slides on the project
[[Media:KVis-deep-learning-video.mov]] | Project Demonstration Video

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:32:51Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
pdf:KVis-deep-learning-project.pdf | Presentation Slides on the project
movie:KVis-deep-learning-video.mov | Project Demonstration Video

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

File:KVis-deep-learning-video.mov

2017-01-13T15:31:48Z

Clisle: Video discussion of our deep learning experience during Project Week 2017

Video discussion of our deep learning experience during Project Week 2017

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:05:39Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
Image:KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation
KVis-deep-learning-project.pdf | Presentation Slides on the project

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

File:KVis-deep-learning-project.pdf

2017-01-13T15:04:11Z

Clisle: Slide presentation describing the project goals and progress

Slide presentation describing the project goals and progress

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:03:17Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
KVis-trained-LeNet-data-augmentation.png|Training performance after data augmentation

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

File:KVis-trained-LeNet-data-augmentation.png

2017-01-13T15:02:35Z

Clisle:

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T15:00:17Z

Clisle: /* Key Investigators */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*Yanling Liu, FNLCR

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-13T14:58:45Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS Amazon instance using NVIDIA's marketplace image before project week
* Prepared the dataset in the style of the MNIST example
* Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework
* Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case
* 3D data was presented without augmentation and yielded better results than 2D alone
* We believe results will further improve when better data augmentation and 3D slice data are used simultaneously
|}

==Background and References==

Explore integration of Web-based imaging workflows with Slicer

2017-01-13T13:23:49Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Slicer connected workflow.png | Workflow to read from SlicerWeb with downstream processing
Image:Outimage.png|Image as received from SlicerWeb
Image:Rotated and blurred.png | Result after running workflow

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
* Exchange data between 3D Slicer and the Flow workflow system
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Use Steve's (work in progress) SlicerWeb Extension to respond to REST requests from Flow
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
* (after project week), expand to work for whole NRRD image that comes from the REST API.
|

* Hosted Slicer locally and was able to reach the REST API, extracting
* Retrieved a 2D Slice from Slicer and automatically ran it through a small workflow
* Next step would be to retrieve named volumes as 3D NRRD format from the SlicerWeb
* Productive discussions were conducted with the NiPype team regarding how to utilize the Flow engine as an interface for NiPype-executed computation
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-13T13:20:45Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Slicer connected workflow.png | Workflow to read from SlicerWeb with downstream processing
Image:Outimage.png|Image as received from SlicerWeb
Image:Rotated and blurred.png | Result after running workflow

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
* Exchange data between 3D Slicer and the Flow workflow system
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Use Steve's (work in progress) SlicerWeb Extension to respond to REST requests from Flow
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
* (after project week), expand to work for whole NRRD image that comes from the REST API.
|

* Hosted Slicer locally and was able to reach the REST API, extracting
* Retrieved a 2D Slice from Slicer and automatically ran it through a small workflow
* Next is to retrieve named volumes as 3D NRRD format volume
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-13T13:18:03Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Flow-screen-shot-01-2017.png |Workflow from web-based Flow system
Image:Outimage.png|Image as received from SlicerWeb
Image:Rotated and blurred.png | Result after running workflow

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
* Exchange data between 3D Slicer and the Flow workflow system
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Use Steve's (work in progress) SlicerWeb Extension to respond to REST requests from Flow
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
* (after project week), expand to work for whole NRRD image that comes from the REST API.
|

* Hosted Slicer locally and was able to reach the REST API, extracting
* Retrieved a 2D Slice from Slicer and automatically ran it through a small workflow
* Next is to retrieve named volumes as 3D NRRD format volume
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

File:Rotated and blurred.png

2017-01-13T13:15:52Z

Clisle: image after workflow steps

image after workflow steps

File:Outimage.png

2017-01-13T13:15:22Z

Clisle: image as retrieved from SlicerWeb.

image as retrieved from SlicerWeb.

File:Slicer connected workflow.png

2017-01-13T13:14:10Z

Clisle: Initial workflow in Flow that pulls imagery data from Slicer using a REST API

Initial workflow in Flow that pulls imagery data from Slicer using a REST API

Explore integration of Web-based imaging workflows with Slicer

2017-01-13T07:27:33Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Flow-screen-shot-01-2017.png |Workflow from web-based Flow system

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
* Exchange data between 3D Slicer and the Flow workflow system
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Use Steve's (work in progress) SlicerWeb Extension to respond to REST requests from Flow
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
* (after project week), expand to work for whole NRRD image that comes from the REST API.
|

* Hosted Slicer locally and was able to reach the REST API, extracting
* Retrieved a 2D Slice from Slicer and automatically ran it through a small workflow
* Next is to retrieve named volumes as 3D NRRD format volume
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-13T07:26:14Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Flow-screen-shot-01-2017.png |Workflow from web-based Flow system

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
* Exchange data between 3D Slicer and the Flow workflow system
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Use Steve's (work in progress) SlicerWeb Extension to respond to REST requests from Flow
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
* (after project week), expand to work for whole NRRD image that comes from the REST API.
|

* Hosted Slicer locally and was able to reach the REST API, extracting
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-09T13:45:51Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Flow-screen-shot-01-2017.png |Workflow from web-based Flow system

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Prototype a Slicer scripted module that loads images using Flow's RestAPI. Flow is a Girder-based app.
* Evaluate SimpleITK vs. scikit-image as imaging toolkit for the workflow system.
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
|

*
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

File:Flow-screen-shot-01-2017.png

2017-01-09T13:44:36Z

Clisle: screenshot from Flow web-based workflow system. For exploratory integration with Slicer

screenshot from Flow web-based workflow system. For exploratory integration with Slicer

Explore integration of Web-based imaging workflows with Slicer

2017-01-09T13:41:50Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
|

* Load a MRML scene into Slicer and read images into Flow by accessing through Slicer's RestAPI
* Prototype a Slicer scripted module that loads images using Flow's RestAPI. Flow is a Girder-based app.
* Evaluate SimpleITK vs. scikit-image as imaging toolkit for the workflow system.
* Connect with Girder-breakout team to discuss data sharing between systems (Slicer, Girder, Flow, etc.)
|

*
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T13:37:06Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS 4 Amazon instance using NVIDIA's marketplace image
* Initial exploration with DIGITS 4 has begun. Preparing the dataset in the style of the MNIST example.
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T13:36:15Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI
Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest T2 images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS 4 Amazon instance using NVIDIA's marketplace image
* Initial exploration with DIGITS 4 has begun. Preparing the dataset in the style of the MNIST example.
|}

==Background and References==

File:Cancer-roi-digits-training-0109.png

2017-01-09T13:34:24Z

Clisle: Results of training of a LeNet DL network on T2 cancer ROI images

Results of training of a LeNet DL network on T2 cancer ROI images

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T13:32:26Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest T2 images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* created a DIGITS 4 Amazon instance using NVIDIA's marketplace image
* Initial exploration with DIGITS 4 has begun. Preparing the dataset in the style of the MNIST example.
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T13:30:55Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest T2 images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* Initial testing with DIGITS 4 is underway. Preparing the dataset in the style of the MNIST example.
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T12:57:30Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.
|

* Initial testing with DIGITS 4 is underway. Preparing the dataset in the style of the MNIST example.
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T04:03:59Z

Clisle: /* Key Investigators */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

*Curt Lisle, KnowledgeVis, LLC
*others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T04:03:35Z

Clisle: /* Project Description */

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

Curt Lisle, KnowledgeVis, LLC
others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
* The dataset consists of a series of 50x50 Region Of Interest images containing cancer lesions and two image series which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T04:01:12Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI

</gallery>

==Key Investigators==

Curt Lisle, KnowledgeVis, LLC
others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Curt has a dataset prepared by colleagues at the Frederick National Lab for Cancer Research to use to train a classifier.
* The library consists of a set of 50x50 PNG images containing cancer lesions and two different 50x50 sets which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T04:00:04Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|Example Cancer ROI]]

</gallery>

==Key Investigators==

Curt Lisle, KnowledgeVis, LLC
others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Curt has a dataset prepared by colleagues at the Frederick National Lab for Cancer Research to use to train a classifier.
* The library consists of a set of 50x50 PNG images containing cancer lesions and two different 50x50 sets which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-09T03:57:25Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
Image:Cancer_roi_Img_00001.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Cancer ROI]]

</gallery>

==Key Investigators==

Curt Lisle, KnowledgeVis, LLC
others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Curt has a dataset prepared by colleagues at the Frederick National Lab for Cancer Research to use to train a classifier.
* The library consists of a set of 50x50 PNG images containing cancer lesions and two different 50x50 sets which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

File:Cancer roi Img 00001.png

2017-01-09T03:54:40Z

Clisle: ROI for cancer detection classifier training

ROI for cancer detection classifier training

2017 Winter Project Week

2017-01-06T14:47:34Z

Clisle: /* Learning and GPUs */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''Transportation:''' Public transportation is highly encouraged, as no parking permits will be issued by MIT. For a list of local garages, please see [[http://web.mit.edu/facilities/transportation/parking/visitors/public_parking.html here]]
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)
* [[2017 Winter Project Week/An open-source tool to classify TMJ OA condyles | An open-source tool to classify TMJ OA condyles]] (Priscille de Dumast, Juan Carlos Prieto, Beatriz Paniagua)
* [[2017 Winter Project Week/DeepInfer| DeepInfer: Open-source Deep Learning Deployment Toolkit]] (Alireza Mehrtash, Mehran Pesteie, Yang (Silvia) Yixin, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
* [[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)

== Web Technologies ==
* [[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine)
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)
*[[2017 Winter Project Week/Electron App to add, navigate and visualize DICOM images | Electron App to add, navigate and visualize DICOM images
]] (Smruti Padhy, Satrajit Ghosh, Mathias Goncalves)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)
* [[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Frederik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DWI_Similarity_Metrics | Identification of information-rich patches in Diffusion-Weighted Images]] (Laurent Chauvin, Fan Zhang, Lauren J. O'Donnell, Matthew Toews)

==Quantitative Imaging Informatics==
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)
== Infrastructure ==
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)
* [[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)
* [[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)
* [[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)

==To be Categorized==

* [[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)
* [[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)
* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)
* [[2017 Winter Project Week/GeodesicSegmentationandLungtumorAnalysis| Geodesic Segmentation and Lung tumor Analysis]] (Patmaa S, Sarthak Pati, Ratheesh k, Mark B, Yong F, Despina K, Ragini V, Christos D)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Burke, Brice :: American University of Antigua College of Medicine
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Fishbaugh, James :: New York University
# Frank, Tobias :: Leibniz Universität Hannover
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Jensen, Henrik G. :: University of Copenhagen
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lasso, Andras :: PerkLab, Queen's University
# Lauer, Rebekka :: Humboldt University Berlin
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Wang, Yaofei :: Brigham and Women's Hospital
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School
# Yang, Yixin :: Brigham and Women's Hospital
# Ye, Wu :: Brigham and Women's Hospital
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Zhang, Yuqian :: Brigham and Women's Hospital
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital

2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

2017-01-06T14:46:44Z

Clisle: Created page with "<gallery> Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|Projects List <!-- Use the "Upload file" link on the left and th..."

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==

Curt Lisle, KnowledgeVis, LLC
others are invited

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
|

* Curt has a dataset prepared by colleagues at the Frederick National Lab for Cancer Research to use to train a classifier.
* The library consists of a set of 50x50 PNG images containing cancer lesions and two different 50x50 sets which do not contain lesions.
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
|

*
|}

==Background and References==

2017 Winter Project Week

2017-01-06T14:41:43Z

Clisle: /* Learning and GPUs */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''Transportation:''' Public transportation is highly encouraged, as no parking permits will be issued by MIT. For a list of local garages, please see [[http://web.mit.edu/facilities/transportation/parking/visitors/public_parking.html here]]
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)
* [[2017 Winter Project Week/An open-source tool to classify TMJ OA condyles | An open-source tool to classify TMJ OA condyles]] (Priscille de Dumast, Juan Carlos Prieto, Beatriz Paniagua)
* [[2017 Winter Project Week/DeepInfer| DeepInfer: Open-source Deep Learning Deployment Toolkit]] (Alireza Mehrtash, Mehran Pesteie, Yang (Silvia) Yixin, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
* [[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer legion classification]] (Curt Lisle)

== Web Technologies ==
* [[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine)
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)
*[[2017 Winter Project Week/Electron App to add, navigate and visualize DICOM images | Electron App to add, navigate and visualize DICOM images
]] (Smruti Padhy, Satrajit Ghosh, Mathias Goncalves)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)
* [[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Frederik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DWI_Similarity_Metrics | Identification of information-rich patches in Diffusion-Weighted Images]] (Laurent Chauvin, Fan Zhang, Lauren J. O'Donnell, Matthew Toews)

==Quantitative Imaging Informatics==
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)
== Infrastructure ==
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)
* [[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)
* [[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)
* [[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)

==To be Categorized==

* [[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)
* [[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)
* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)
* [[2017 Winter Project Week/GeodesicSegmentationandLungtumorAnalysis| Geodesic Segmentation and Lung tumor Analysis]] (Patmaa S, Sarthak Pati, Ratheesh k, Mark B, Yong F, Despina K, Ragini V, Christos D)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Burke, Brice :: American University of Antigua College of Medicine
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Fishbaugh, James :: New York University
# Frank, Tobias :: Leibniz Universität Hannover
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Jensen, Henrik G. :: University of Copenhagen
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lasso, Andras :: PerkLab, Queen's University
# Lauer, Rebekka :: Humboldt University Berlin
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Wang, Yaofei :: Brigham and Women's Hospital
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School
# Yang, Yixin :: Brigham and Women's Hospital
# Ye, Wu :: Brigham and Women's Hospital
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Zhang, Yuqian :: Brigham and Women's Hospital
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital

2017 Winter Project Week

2017-01-06T14:41:26Z

Clisle: /* Learning and GPUs */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''Transportation:''' Public transportation is highly encouraged, as no parking permits will be issued by MIT. For a list of local garages, please see [[http://web.mit.edu/facilities/transportation/parking/visitors/public_parking.html here]]
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)
* [[2017 Winter Project Week/An open-source tool to classify TMJ OA condyles | An open-source tool to classify TMJ OA condyles]] (Priscille de Dumast, Juan Carlos Prieto, Beatriz Paniagua)
* [[2017 Winter Project Week/DeepInfer| DeepInfer: Open-source Deep Learning Deployment Toolkit]] (Alireza Mehrtash, Mehran Pesteie, Yang (Silvia) Yixin, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
* [[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | /Evaluate Deep Learning for binary cancer legion classification]] (Curt Lisle)

== Web Technologies ==
* [[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine)
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)
*[[2017 Winter Project Week/Electron App to add, navigate and visualize DICOM images | Electron App to add, navigate and visualize DICOM images
]] (Smruti Padhy, Satrajit Ghosh, Mathias Goncalves)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)
* [[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Frederik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DWI_Similarity_Metrics | Identification of information-rich patches in Diffusion-Weighted Images]] (Laurent Chauvin, Fan Zhang, Lauren J. O'Donnell, Matthew Toews)

==Quantitative Imaging Informatics==
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)
== Infrastructure ==
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)
* [[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)
* [[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)
* [[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)

==To be Categorized==

* [[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)
* [[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)
* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)
* [[2017 Winter Project Week/GeodesicSegmentationandLungtumorAnalysis| Geodesic Segmentation and Lung tumor Analysis]] (Patmaa S, Sarthak Pati, Ratheesh k, Mark B, Yong F, Despina K, Ragini V, Christos D)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Burke, Brice :: American University of Antigua College of Medicine
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Fishbaugh, James :: New York University
# Frank, Tobias :: Leibniz Universität Hannover
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Jensen, Henrik G. :: University of Copenhagen
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lasso, Andras :: PerkLab, Queen's University
# Lauer, Rebekka :: Humboldt University Berlin
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Wang, Yaofei :: Brigham and Women's Hospital
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School
# Yang, Yixin :: Brigham and Women's Hospital
# Ye, Wu :: Brigham and Women's Hospital
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Zhang, Yuqian :: Brigham and Women's Hospital
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital

2017 Winter Project Week

2017-01-03T16:36:13Z

Clisle: /* Web Technologies */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)

== Web Technologies ==
* [[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Gosh, Hans Meine)
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Frederik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)

== Infrastructure ==

* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua)

==To be Categorized==

* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)
* [[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)
* [[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Ye, Wu :: Brigham and Women's Hospital
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital

2017 Winter Project Week/dcmqi

2017-01-03T16:13:25Z

Clisle: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]


</gallery>

==Key Investigators==

* Andrey Fedorov, BWH
* Christian Herz, BWH
* Steve Pieper, Isomics
* Jean-Christophe Fillion-Robin, Kitware
* Andras Lasso, Queen's
* Csaba Pinter, Queen's
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Introduce dcmqi to the community
* demonstrate Quantitative Reporting extension
* improve documentation
* discuss next steps and specific open issues
|

* Present, meet collaborators...
* discuss pros and cons of integration as an external project into Slicer, and agree on the plan
* discuss the process of integration of new segmentation contexts by the user
* Terminology Editor
* passing DICOM instances to the converters
* support of units/quantities in Slicer
* communication of measurements tables and integrated support into Slicer Table node (CSV/JSON/other approaches - see https://github.com/QIICR/QuantitativeReporting/issues/105)
|

*
|}

==Background and References==
* dcmqi home page: http://github.com/qiicr/dcmqi
* dcmqi Slicer extension: https://www.slicer.org/wiki/Documentation/Nightly/Extensions/DCMQI
* DICOM4QI at RSNA2016: https://fedorov.gitbooks.io/rsna2016-qirr-dicom4qi/content/

Explore integration of Web-based imaging workflows with Slicer

2017-01-03T16:00:32Z

Clisle:

<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
|

* Evluate SimpleITK vs. scikit-image as imaging toolkit for the workflow system.
* Connect with Girder-breakout team to discuss data sharing between systems
|

*
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-03T15:59:51Z

Clisle: /* Background and References */

_NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system (Flow, developed by Kitware and KnowledgeVis, LLC)
|

* Evluate SimpleITK vs. scikit-image as imaging toolkit for the workflow system.
* Connect with Girder-breakout team to discuss data sharing between systems
|

*
|}

==Background and References==

The Flow source code is available here: https://github.com/Kitware/flow

Explore integration of Web-based imaging workflows with Slicer

2017-01-03T15:59:24Z

Clisle: /* Project Description */

Explore integration of Web-based imaging workflows with Slicer

2017-01-03T15:58:44Z

Clisle:

_NOTOC__
<gallery>
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==
* Curt Lisle, KnowledgeVis, LLC

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Explore how Slicer can interact with an existing web-based, open-source workflow system ([[https://github.com/Kitware/flow|Flow]], developed by Kitware and KnowledgeVis, LLC)
|

* Evluate SimpleITK vs. scikit-image as imaging toolkit for the workflow system.
* Connect with Girder-breakout team to discuss data sharing between systems
|

*
|}

==Background and References==

Explore integration of Web-based imaging workflows with Slicer

2017-01-03T15:58:43Z

Clisle: Created page with "_NOTOC__ <gallery> Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|Projects List <!-- Use the "Upload file" link on the le..."

2017 Winter Project Week

2017-01-03T15:52:27Z

Clisle: /* Web Technologies */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.''''' 

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)

== Web Technologies ==
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Frederik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)

== Infrastructure ==
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)

==To be Categorized==

* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Ye, Wu :: Brigham and Women's Hospital
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital