https://www.na-mic.org/w/api.php?action=feedcontributions&user=Mpc25&feedformat=atomNAMIC Wiki - User contributions [en]2024-03-28T11:59:37ZUser contributionsMediaWiki 1.33.0https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=949802017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-13T07:12:44Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
Image:Chae2015-Fig4.png<br />
File:5.JPG<br />
File:6.JPG<br />
</gallery><br />
<br />
==Key Investigators==<br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada)<br />
* David Garcia (Queen’s University, Canada)<br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
<br />
* Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
** Volumetric analysis based on surface scans and pre-op volumetric images<br />
** 3D-printed surgical planning template <br />
* Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques. <br />
* Implement interface for inexpensive 3D scanners (Intel RealSense cameras) in Slicer<br />
<br />
|<br />
<!-- Approach and Plan bullet points --><br />
<br />
* Volumetric analysis:<br />
** Current method [Chae2014]: We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation. <br />
** We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques <br />
<br />
* 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.<br />
<br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week --><br />
<br />
Outcomes of our project from this year’s project week is outlined below: <br />
<br />
# Volumetric analysis <br />
## Purchase Intel RealSense 3D scanner at Monash University and use it for planning breast reconstructions<br />
## Image patient’s arms in Akimbo <br />
## Create breast’s posterior plane by identifying breast margins using fiducials <br />
### However, there is a room for investigation here to see if an artificial “flat” posterior plane is going to be more clinically useful since it accounts for potential asymmetry in the chest wall (i.e. ribs, pectoralis muscles)<br />
## Derive volume using “Segment Statistics” module <br />
## Apply into case series for publication <br />
<br />
# 3D printed surgical planning template <br />
## The new Segmentation Editor module has streamlined everything<br />
## What we need to segment is the DIEA (deep inferior epigastric artery) from its origin (i.e. common femoral artery), DIEP (deep inferior epigastric artery perforator), rectus abdominis muscle, and overlying skin <br />
## Segmenting skin: threshold, regiongrowing by ~5 mm, subtract <br />
## Segmenting muscle: threshold paint, draw out the muscle at various points on axials, automatically fill in the gaps <br />
## Segmenting DIEA and DIEP: VMTK, region growing, still work to do to streamline this process <br />
<br />
* Current sources of funding and collaborative efforts elsewhere:<br />
** MIME (Monash Institute of Medical Engineering) Seedfund grant (for 3D bioprinting), Monash University (Funding ID M17001/3167528) <br />
** Development of a prototype volumetric analysis tool for CT and MR guided 3D printing<br />
** Development of a 3D printed templating technique for vascular mapping in reconstructive surgery<br />
** Founding of The Peninsula 3D Printing Laboratory, for surgical 3D printing clinical and research applications.<br />
** Collaborative 3D printing research projects with:<br />
*** Monash Institute of Medical Engineering (MIME), Materials Engineering Department, Monash University - "3D-bioprinted scaffold of trapezium in basal thumb arthritis management".<br />
*** Hudson Institute of Medical Research, Monash University – “3D Bioprinting in Reconstructive Surgery”.<br />
*** St Andrew’s Centre for Plastic and Reconstructive Surgery, Broomfield Hospital, UK – “Volumetric and Imaging Analysis in Breast Reconstruction”.<br />
<br />
<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --><br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
* Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.<br />
* Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
* Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
* Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
* Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=File:5.JPG&diff=94978File:5.JPG2017-01-13T07:10:43Z<p>Mpc25: Mpc25 uploaded a new version of File:5.JPG</p>
<hr />
<div>DIEP and overlying skin</div>Mpc25https://www.na-mic.org/w/index.php?title=File:5.JPG&diff=94979File:5.JPG2017-01-13T07:10:43Z<p>Mpc25: Mpc25 uploaded a new version of File:5.JPG</p>
<hr />
<div>DIEP and overlying skin</div>Mpc25https://www.na-mic.org/w/index.php?title=File:5.JPG&diff=94977File:5.JPG2017-01-13T07:10:41Z<p>Mpc25: DIEP and overlying skin</p>
<hr />
<div>DIEP and overlying skin</div>Mpc25https://www.na-mic.org/w/index.php?title=File:6.JPG&diff=94976File:6.JPG2017-01-13T07:10:02Z<p>Mpc25: DIEA and DIEP</p>
<hr />
<div>DIEA and DIEP</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=949752017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-13T07:08:07Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
Image:Chae2015-Fig4.png<br />
</gallery><br />
<br />
==Key Investigators==<br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada)<br />
* David Garcia (Queen’s University, Canada)<br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
<br />
* Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
** Volumetric analysis based on surface scans and pre-op volumetric images<br />
** 3D-printed surgical planning template <br />
* Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques. <br />
* Implement interface for inexpensive 3D scanners (Intel RealSense cameras) in Slicer<br />
<br />
|<br />
<!-- Approach and Plan bullet points --><br />
<br />
* Volumetric analysis:<br />
** Current method [Chae2014]: We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation. <br />
** We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques <br />
<br />
* 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.<br />
<br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week --><br />
<br />
Outcomes of our project from this year’s project week is outlined below: <br />
<br />
# Volumetric analysis <br />
## Purchase Intel RealSense 3D scanner at Monash University and use it for planning breast reconstructions<br />
## Image patient’s arms in Akimbo <br />
## Create breast’s posterior plane by identifying breast margins using fiducials <br />
### However, there is a room for investigation here to see if an artificial “flat” posterior plane is going to be more clinically useful since it accounts for potential asymmetry in the chest wall (i.e. ribs, pectoralis muscles)<br />
## Derive volume using “Segment Statistics” module <br />
## Apply into case series for publication <br />
<br />
# 3D printed surgical planning template <br />
## The new Segmentation Editor module has streamlined everything<br />
## What we need to segment is the DIEA (deep inferior epigastric artery) from its origin (i.e. common femoral artery), DIEP (deep inferior epigastric artery perforator), rectus abdominis muscle, and overlying skin <br />
## Segmenting skin: threshold, regiongrowing by ~5 mm, subtract <br />
## Segmenting muscle: threshold paint, draw out the muscle at various points on axials, automatically fill in the gaps <br />
## Segmenting DIEA and DIEP: VMTK, region growing, still work to do to streamline this process <br />
<br />
* Current sources of funding and collaborative efforts elsewhere:<br />
** MIME (Monash Institute of Medical Engineering) Seedfund grant (for 3D bioprinting), Monash University (Funding ID M17001/3167528) <br />
** Development of a prototype volumetric analysis tool for CT and MR guided 3D printing<br />
** Development of a 3D printed templating technique for vascular mapping in reconstructive surgery<br />
** Founding of The Peninsula 3D Printing Laboratory, for surgical 3D printing clinical and research applications.<br />
** Collaborative 3D printing research projects with:<br />
*** Monash Institute of Medical Engineering (MIME), Materials Engineering Department, Monash University - "3D-bioprinted scaffold of trapezium in basal thumb arthritis management".<br />
*** Hudson Institute of Medical Research, Monash University – “3D Bioprinting in Reconstructive Surgery”.<br />
*** St Andrew’s Centre for Plastic and Reconstructive Surgery, Broomfield Hospital, UK – “Volumetric and Imaging Analysis in Breast Reconstruction”.<br />
<br />
<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --><br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
* Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.<br />
* Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
* Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
* Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
* Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=949742017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-13T07:07:30Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
Image:Chae2015-Fig4.png<br />
</gallery><br />
<br />
==Key Investigators==<br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada)<br />
* David Garcia (Queen’s University, Canada)<br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
<br />
* Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
** Volumetric analysis based on surface scans and pre-op volumetric images<br />
** 3D-printed surgical planning template <br />
* Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques. <br />
* Implement interface for inexpensive 3D scanners (Intel RealSense cameras) in Slicer<br />
<br />
|<br />
<!-- Approach and Plan bullet points --><br />
<br />
* Volumetric analysis:<br />
** Current method [Chae2014]: We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation. <br />
** We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques <br />
<br />
* 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.<br />
<br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week --><br />
<br />
Outcomes of our project from this year’s project week is outlined below: <br />
<br />
# Volumetric analysis <br />
## Purchase Intel RealSense 3D scanner at Monash University and use it for planning breast reconstructions<br />
## Image patient’s arms in Akimbo <br />
## Create breast’s posterior plane by identifying breast margins using fiducials <br />
### However, there is a room for investigation here to see if an artificial “flat” posterior plane is going to be more clinically useful since it accounts for potential asymmetry in the chest wall (i.e. ribs, pectoralis muscles)<br />
## Derive volume using “Segment Statistics” module <br />
## Apply into case series for publication <br />
<br />
# 3D printed surgical planning template <br />
## The new Segmentation Editor module has streamlined everything<br />
## What we need to segment is the DIEA (deep inferior epigastric artery) from its origin (i.e. common femoral artery), DIEP (deep inferior epigastric artery perforator), rectus abdominis muscle, and overlying skin <br />
## Segmenting skin: threshold, regiongrowing by ~5 mm, subtract <br />
## Segmenting muscle: threshold paint, draw out the muscle at various points on axials, automatically fill in the gaps <br />
## Segmenting DIEA and DIEP: VMTK, region growing, still work to do to streamline this process <br />
<br />
* Current sources of funding and collaborative efforts elsewhere:<br />
** MIME (Monash Institute of Medical Engineering) Seedfund grant (for 3D bioprinting), Monash University (Funding ID M17001/3167528) <br />
** Development of a prototype volumetric analysis tool for CT and MR guided 3D printing<br />
** Development of a 3D printed templating technique for vascular mapping in reconstructive surgery<br />
** Founding of The Peninsula 3D Printing Laboratory, for surgical 3D printing clinical and research applications.<br />
** Collaborative 3D printing research projects with:<br />
*** Monash Institute of Medical Engineering (MIME), Materials Engineering Department, Monash University - "3D-bioprinted scaffold of trapezium in basal thumb arthritis management".<br />
*** Hudson Institute of Medical Research, Monash University – “3D Bioprinting in Reconstructive Surgery”.<br />
*** St Andrew’s Centre for Plastic and Reconstructive Surgery, Broomfield Hospital, UK – “Volumetric and Imaging Analysis in Breast Reconstruction”.<br />
<br />
<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --><br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
* [Chae2014] Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.<br />
* Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
* Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
* Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
* Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=947512017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-10T12:20:54Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
Image:Chae2015-Fig4.png<br />
</gallery><br />
<br />
==Key Investigators==<br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada)<br />
* David Garcia (Queen’s University, Canada)<br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
<br />
* Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
** Volumetric analysis based on surface scans and pre-op volumetric images<br />
** 3D-printed surgical planning template <br />
* Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques. <br />
* Implement interface for inexpensive 3D scanners (Intel RealSense cameras) in Slicer<br />
<br />
|<br />
<!-- Approach and Plan bullet points --><br />
<br />
* Volumetric analysis:<br />
** Current method [Chae2014]: We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation. <br />
** We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques <br />
<br />
* 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.<br />
<br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week --><br />
<br />
* MIME (Monash Institute of Medical Engineering) Seedfund grant (for 3D bioprinting), Monash University (Funding ID M17001/3167528) <br />
* Development of a prototype volumetric analysis tool for CT and MR guided 3D printing<br />
* Development of a 3D printed templating technique for vascular mapping in reconstructive surgery<br />
* Founding of The Peninsula 3D Printing Laboratory, for surgical 3D printing clinical and research applications.<br />
* Collaborative 3D printing research projects with:<br />
** Monash Institute of Medical Engineering (MIME), Materials Engineering Department, Monash University - "3D-bioprinted scaffold of trapezium in basal thumb arthritis management".<br />
** Hudson Institute of Medical Research, Monash University – “3D Bioprinting in Reconstructive Surgery”.<br />
** St Andrew’s Centre for Plastic and Reconstructive Surgery, Broomfield Hospital, UK – “Volumetric and Imaging Analysis in Breast Reconstruction”.<br />
<br />
<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --><br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
* [Chae2014] Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.<br />
* Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
* Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
* Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
* Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=945252017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-09T03:38:36Z<p>Mpc25: </p>
<hr />
<div>Key Investigators <br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada) <br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
Project description <br />
<br />
Objective <br />
At our institute, we have identified 2 useful tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
# Volumetric analysis <br />
# 3D-printed surgical planning template <br />
However, our method requires extensive manual handling and is subsequently slow. In order for us to deliver therapy in a timely manner and upscale our techniques to more patients, we are aiming to develop automated or semi-automated techniques. <br />
<br />
Approach and plan <br />
<br />
1. Volumetric analysis:<br />
<br />
Our volumetric analysis technique has been described previously in this paper(1). But briefly, we can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation. <br />
<br />
We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques <br />
<br />
(1) Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.<br />
<br />
2. 3D printed surgical planning template: <br />
<br />
In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.<br />
<br />
<br />
Progress and next steps <br />
<br />
Background and references <br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
# Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
# Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
# Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
# Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=944692017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-07T09:52:51Z<p>Mpc25: </p>
<hr />
<div>Key Investigators <br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada) <br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
Project description <br />
<br />
Objective <br />
At our institute, we have identified 2 useful tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
# Volumetric analysis <br />
# 3D-printed surgical planning template <br />
However, our method requires extensive manual handling and is subsequently slow. In order for us to deliver therapy in a timely manner and upscale our techniques to more patients, we are aiming to develop automated or semi-automated techniques. <br />
<br />
Approach and plan <br />
<br />
Progress and next steps <br />
<br />
Background and references <br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
# Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
# Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
# Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
# Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3DSurgicalPlanningBreastReconstruction&diff=944682017 Winter Project Week/3DSurgicalPlanningBreastReconstruction2017-01-07T09:20:10Z<p>Mpc25: Created page with "Key Investigators * Michael Chae (Monash University, Australia) * Andras Lasso (Queen’s University, Canada) * Julian Smith (Monash University, Australia) * Warren Rozen..."</p>
<hr />
<div>Key Investigators <br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada) <br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
Project description <br />
<br />
Objective <br />
At our institute, we have identified 2 useful tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
# Volumetric analysis <br />
# 3D-printed surgical planning template <br />
However, our method requires extensive manual handling and subsequently slow. In order for us to deliver therapy in timely manner and upscale our techniques to more patients, we are aiming to develop automated or semi-automated techniques. <br />
<br />
Approach and plan <br />
<br />
Progress and next steps <br />
<br />
Background and references <br />
<br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)<br />
<br />
# Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
# Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
# Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
# Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week&diff=944672017 Winter Project Week2017-01-07T09:18:55Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
[[image:PW-Winter2017.png|300px]]<br />
<br />
=Welcome to the web page for the 24th Project Week!=<br />
<br />
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].<br />
<br />
==Logistics==<br />
<br />
*'''Dates:''' January 9-13, 2017.<br />
*'''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)<br />
*'''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]<br />
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.<br />
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.<br />
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]<br />
<br />
== Introduction ==<br />
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.<br />
<br />
[[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. <br />
<br />
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.<br />
<br />
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]. <br />
<br />
A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].<br />
<br />
Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]<br />
<br />
==Conference Calls for Preparation==<br />
<br />
Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].<br />
<br />
==Calendar==<br />
<br />
'''''<font color="maroon">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.</font>'''''<br><br />
<br />
<br />
{{#widget:Google Calendar<br />
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com<br />
|timezone=America/New_York&dates=20170108%2F20170114<br />
|title=NAMIC Winter Project Week<br />
|view=WEEK<br />
|dates=20170108/20170114<br />
}}<br />
<br />
iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics<br />
<br />
='''Projects'''=<br />
<br />
*Use this [[2017_Project_Week_Template | Updated Template for project pages]]<br />
<br />
== Learning and GPUs ==<br />
#[[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)<br />
# [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)<br />
#[[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)<br />
#[[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)<br />
#[[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)<br />
#[[2017 Winter Project Week/Diffusely abnormal white matter segmentation with 3d U-net| Diffusely abnormal white matter segmentation with 3d U-net]] (Mohsen Ghafoorrian, Bram Platel, Sandy Wells, Tina Kapur)<br />
<br />
== Web Technologies ==<br />
#[[2017_Winter_Project_Week/MedicalVisualizerUsingParaViewWeb | Medical Visualizer using ParaViewWeb]] (Teodora Szasz)<br />
#[[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine) <br />
#[[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)<br />
#[[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper, Teodora Szasz)<br />
#[[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)<br />
#[[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)<br />
#[[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)<br />
#[[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)<br />
#[[2017 Winter Project Week/AMI: A 3D Medical Imaging Javascript Library | AMI: A 3D Medical Imaging Javascript Library]] (Rudolph Pienaar, Teodora Szasz)<br />
<br />
== IGT: Navigation, Robotics, Surgical Planning ==<br />
#[[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)<br />
#[[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)<br />
#[[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)<br />
#[[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, )<br />
#[[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)<br />
#[[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)<br />
#[[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)<br />
#[[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)<br />
#[[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)<br />
<br />
==dMRI==<br />
#[[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)<br />
#[[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Fredrik Westin)<br />
#[[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)<br />
#[[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)<br />
#[[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)<br />
<br />
==Quantitative Imaging Informatics==<br />
#[[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)<br />
#[[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)<br />
#[[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)<br />
#[[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)<br />
<br />
== Visualization ==<br />
#[[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)<br />
<br />
== Infrastructure ==<br />
#[[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)<br />
#[[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)<br />
#[[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)<br />
#[[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)<br />
#[[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)<br />
#[[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)<br />
<br />
==Shape Analysis==<br />
#[[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)<br />
#[[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)<br />
<br />
==To be Categorized==<br />
#[[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)<br />
#[[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)<br />
#[[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)<br />
#[[2017 Winter Project Week/3DSurgicalPlanningBreastReconstruction| 3D surgical planning solution for autologous breast reconstruction]] (Michael Chae, Andras Lasso, Julian Smith, Warren Rozen, David Hunter-Smith)<br />
<br />
= '''Registrants''' =<br />
<br />
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].<br />
<br />
# Aman Shboul, Zaina :: Old Dominion University<br />
# Aerts, Hugo :: DFCI-Harvard<br />
# Alam, Mahbubul :: Old Dominion University<br />
# Anderson, Peter :: Retired<br />
# Andruejol, Johan :: Kitware, Inc.<br />
# Bakeng, Janne Beate :: SINTEF<br />
# Beers, Andrew :: Massachusetts General Hospital<br />
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital<br />
# Bremer, Erich :: Stony Brook University<br />
# Burke, Brice :: American University of Antigua College of Medicine<br />
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital<br />
# Chae, Michael :: Monash University<br />
# Chauvin, Laurent :: ETS<br />
# Dalca, Adrian :: Massachusetts Institute of Technology<br />
# DiPrima, Tammy :: Stony Brook University<br />
# Drouin, Simon :: Montreal Neurological Institute<br />
# Fan, Zhipeng :: Brigham and Women's Hospital<br />
# Fedorov, Andriy :: Brigham and Women's Hospital<br />
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.<br />
# Fishbaugh, James :: New York University<br />
# Frank, Tobias :: Leibniz Universität Hannover<br />
# Frisken, Sarah :: Brigham and Women's Hospital<br />
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid<br />
# Ghafoorian, Mohsen :: Brigham and Women's Hospital<br />
# Ghosh, Satrajit :: Massachusetts Institute of Technology<br />
# Girault, Alexis :: Kitware, Inc.<br />
# Golland, Polina :: Massachusetts Institute of Technology<br />
# Gollub, Randy :: Massachusetts General Hospital<br />
# Goncalves, Mathias :: Massachusetts Institute of Technology<br />
# Gong, Shun :: Brigham and Women's Hospital<br />
# Guerrier de Dumast, Priscille :: University of Michigan<br />
# Harris, Gordon :: Massachusetts General Hospital<br />
# Helba, Brian :: Kitware, Inc.<br />
# Herz, Christian :: Brigham and Women's Hospital<br />
# Hong, Sungmin :: New York University<br />
# Hosny, Ahmed :: Dana-Farber<br />
# Jagadeesan, Jayender :: Brigham and Women's Hospital<br />
# Jarecka, Dorota :: Massachusetts Institute of Technology<br />
# Jensen, Henrik G. :: University of Copenhagen<br />
# Kaczmarzyk, Jakub :: Massachusetts Institute of Technology<br />
# Kapur, Tina :: Brigham and Women's Hospital<br />
# Kennedy, David :: UMass Medical School<br />
# Kikinis, Ron :: Brigham and Women's Hospital<br />
# Lasso, Andras :: PerkLab, Queen's University<br />
# Lauer, Rebekka :: Humboldt University Berlin<br />
# Lisle, Curtis :: KnowledgeVis, LLC<br />
# Mastrogiacomo, Katie :: Brigham and Women's Hospital<br />
# Mateus, D. :: TUM<br />
# Mehrtash, Alireza :: Brigham and Women's Hospital<br />
# Meine, Hans :: University of Bremen<br />
# Meyer, Anneke :: University of Magdeburg<br />
# Miller, James :: GE Research<br />
# Mirabel, Clement :: University of Michigan<br />
# Nitsch, Jennifer :: University of Bremen<br />
# Norton, Isaiah :: Brigham and Women's Hospital<br />
# O'Donnell, Lauren :: Brigham and Women's Hospital<br />
# Oram, Louise :: The Intervention Centre-Oslo University Hospital<br />
# Padhy, Smruti :: Massachusetts Institute of Technology<br />
# Paniagua, Beatriz :: Kitware, Inc.<br />
# Parmar, Chintan :: DFCI-Harvard Medical School<br />
# Peled, Sharon :: Brigham and Women's Hospital<br />
# Pieper, Steve :: Isomics, Inc.<br />
# Pinter, Csaba :: Queen's University<br />
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School<br />
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School<br />
# Rankin, Adam :: Robarts Research Institute<br />
# Rheault, Francois :: Université de Sherbrooke<br />
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin<br />
# Sharp, Gregory :: Massachusetts General Hospital<br />
# Sridharan, Patmaa :: University of Pennsylvania-CBICA<br />
# Szasz, Teodora :: University of Chicago<br />
# Unadkat, Prashin :: Brigham and Women's Hospital<br />
# Van Griethuysen , Joost :: Netherlands Cancer Institute<br />
# Vidyaratne, Lasitha :: Old Dominion University<br />
# Wang, Yaofei :: Brigham and Women's Hospital<br />
# Wang, Ziyang :: Brigham and Women's Hospital<br />
# Wei, Dawei :: Brigham and Women's Hospital<br />
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School<br />
# Xu, Wanxin :: Brigham and Women's Hospital<br />
# Yang, Yixin :: Brigham and Women's Hospital<br />
# Ye, Wu :: Brigham and Women's Hospital<br />
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy<br />
# Zeleznik, Roman :: DFCI<br />
# Zhang, Fan :: Brigham and Women's Hospital<br />
# Zhang, Miaomiao :: Massachusetts Institute of Technology<br />
# Zhang, Yuqian :: Brigham and Women's Hospital<br />
# Zhou, Haoyin :: Brigham and Women's Hospital<br />
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week/3D_surgical_planning_solution_for_autologous_breast_reconstruction&diff=944662017 Winter Project Week/3D surgical planning solution for autologous breast reconstruction2017-01-07T09:16:39Z<p>Mpc25: Created page with "Key Investigators: * Michael Chae (Monash University, Australia) * Andras Lasso (Queen’s University, Canada) * Julian Smith (Monash University, Australia) * Warren Rozen..."</p>
<hr />
<div>Key Investigators: <br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada) <br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia) <br />
<br />
Project Description: <br />
<br />
At our institute, we have identified 2 useful tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer: <br />
# Volumetric analysis <br />
# 3D-printed surgical planning template <br />
However, our method requires extensive manual handling and subsequently slow. In order for us to deliver therapy in timely manner and upscale our techniques to more patients, we are aiming to develop automated or semi-automated techniques. <br />
<br />
Approach and Plan:<br />
<br />
Progress and Next Steps: <br />
<br />
Background and References: <br />
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. <br />
# Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.<br />
# Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.<br />
# Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.<br />
# Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Winter_Project_Week&diff=944652017 Winter Project Week2017-01-07T09:14:18Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
[[image:PW-Winter2017.png|300px]]<br />
<br />
=Welcome to the web page for the 24th Project Week!=<br />
<br />
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].<br />
<br />
==Logistics==<br />
<br />
*'''Dates:''' January 9-13, 2017.<br />
*'''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)<br />
*'''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]<br />
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.<br />
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.<br />
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]<br />
<br />
== Introduction ==<br />
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.<br />
<br />
[[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. <br />
<br />
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.<br />
<br />
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]. <br />
<br />
A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].<br />
<br />
Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]<br />
<br />
==Conference Calls for Preparation==<br />
<br />
Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].<br />
<br />
==Calendar==<br />
<br />
'''''<font color="maroon">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.</font>'''''<br><br />
<br />
<br />
{{#widget:Google Calendar<br />
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com<br />
|timezone=America/New_York&dates=20170108%2F20170114<br />
|title=NAMIC Winter Project Week<br />
|view=WEEK<br />
|dates=20170108/20170114<br />
}}<br />
<br />
iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics<br />
<br />
='''Projects'''=<br />
<br />
*Use this [[2017_Project_Week_Template | Updated Template for project pages]]<br />
<br />
== Learning and GPUs ==<br />
#[[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)<br />
# [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)<br />
#[[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)<br />
#[[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)<br />
#[[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)<br />
#[[2017 Winter Project Week/Diffusely abnormal white matter segmentation with 3d U-net| Diffusely abnormal white matter segmentation with 3d U-net]] (Mohsen Ghafoorrian, Bram Platel, Sandy Wells, Tina Kapur)<br />
<br />
== Web Technologies ==<br />
#[[2017_Winter_Project_Week/MedicalVisualizerUsingParaViewWeb | Medical Visualizer using ParaViewWeb]] (Teodora Szasz)<br />
#[[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine) <br />
#[[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)<br />
#[[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper, Teodora Szasz)<br />
#[[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)<br />
#[[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)<br />
#[[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)<br />
#[[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)<br />
#[[2017 Winter Project Week/AMI: A 3D Medical Imaging Javascript Library | AMI: A 3D Medical Imaging Javascript Library]] (Rudolph Pienaar, Teodora Szasz)<br />
<br />
== IGT: Navigation, Robotics, Surgical Planning ==<br />
#[[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)<br />
#[[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)<br />
#[[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)<br />
#[[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, )<br />
#[[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)<br />
#[[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)<br />
#[[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)<br />
#[[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)<br />
#[[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)<br />
<br />
==dMRI==<br />
#[[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)<br />
#[[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Fredrik Westin)<br />
#[[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)<br />
#[[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)<br />
#[[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)<br />
<br />
==Quantitative Imaging Informatics==<br />
#[[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)<br />
#[[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)<br />
#[[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)<br />
#[[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)<br />
<br />
== Visualization ==<br />
#[[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)<br />
<br />
== Infrastructure ==<br />
#[[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)<br />
#[[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)<br />
#[[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)<br />
#[[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)<br />
#[[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)<br />
#[[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)<br />
<br />
==Shape Analysis==<br />
#[[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)<br />
#[[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)<br />
<br />
==To be Categorized==<br />
#[[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)<br />
#[[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)<br />
#[[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)<br />
#[[2017 Winter Project Week/3D surgical planning solution for autologous breast reconstruction]] (Michael Chae, Andras Lasso, Julian Smith, Warren Rozen, David Hunter-Smith)<br />
<br />
= '''Registrants''' =<br />
<br />
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].<br />
<br />
# Aman Shboul, Zaina :: Old Dominion University<br />
# Aerts, Hugo :: DFCI-Harvard<br />
# Alam, Mahbubul :: Old Dominion University<br />
# Anderson, Peter :: Retired<br />
# Andruejol, Johan :: Kitware, Inc.<br />
# Bakeng, Janne Beate :: SINTEF<br />
# Beers, Andrew :: Massachusetts General Hospital<br />
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital<br />
# Bremer, Erich :: Stony Brook University<br />
# Burke, Brice :: American University of Antigua College of Medicine<br />
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital<br />
# Chae, Michael :: Monash University<br />
# Chauvin, Laurent :: ETS<br />
# Dalca, Adrian :: Massachusetts Institute of Technology<br />
# DiPrima, Tammy :: Stony Brook University<br />
# Drouin, Simon :: Montreal Neurological Institute<br />
# Fan, Zhipeng :: Brigham and Women's Hospital<br />
# Fedorov, Andriy :: Brigham and Women's Hospital<br />
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.<br />
# Fishbaugh, James :: New York University<br />
# Frank, Tobias :: Leibniz Universität Hannover<br />
# Frisken, Sarah :: Brigham and Women's Hospital<br />
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid<br />
# Ghafoorian, Mohsen :: Brigham and Women's Hospital<br />
# Ghosh, Satrajit :: Massachusetts Institute of Technology<br />
# Girault, Alexis :: Kitware, Inc.<br />
# Golland, Polina :: Massachusetts Institute of Technology<br />
# Gollub, Randy :: Massachusetts General Hospital<br />
# Goncalves, Mathias :: Massachusetts Institute of Technology<br />
# Gong, Shun :: Brigham and Women's Hospital<br />
# Guerrier de Dumast, Priscille :: University of Michigan<br />
# Harris, Gordon :: Massachusetts General Hospital<br />
# Helba, Brian :: Kitware, Inc.<br />
# Herz, Christian :: Brigham and Women's Hospital<br />
# Hong, Sungmin :: New York University<br />
# Hosny, Ahmed :: Dana-Farber<br />
# Jagadeesan, Jayender :: Brigham and Women's Hospital<br />
# Jarecka, Dorota :: Massachusetts Institute of Technology<br />
# Jensen, Henrik G. :: University of Copenhagen<br />
# Kaczmarzyk, Jakub :: Massachusetts Institute of Technology<br />
# Kapur, Tina :: Brigham and Women's Hospital<br />
# Kennedy, David :: UMass Medical School<br />
# Kikinis, Ron :: Brigham and Women's Hospital<br />
# Lasso, Andras :: PerkLab, Queen's University<br />
# Lauer, Rebekka :: Humboldt University Berlin<br />
# Lisle, Curtis :: KnowledgeVis, LLC<br />
# Mastrogiacomo, Katie :: Brigham and Women's Hospital<br />
# Mateus, D. :: TUM<br />
# Mehrtash, Alireza :: Brigham and Women's Hospital<br />
# Meine, Hans :: University of Bremen<br />
# Meyer, Anneke :: University of Magdeburg<br />
# Miller, James :: GE Research<br />
# Mirabel, Clement :: University of Michigan<br />
# Nitsch, Jennifer :: University of Bremen<br />
# Norton, Isaiah :: Brigham and Women's Hospital<br />
# O'Donnell, Lauren :: Brigham and Women's Hospital<br />
# Oram, Louise :: The Intervention Centre-Oslo University Hospital<br />
# Padhy, Smruti :: Massachusetts Institute of Technology<br />
# Paniagua, Beatriz :: Kitware, Inc.<br />
# Parmar, Chintan :: DFCI-Harvard Medical School<br />
# Peled, Sharon :: Brigham and Women's Hospital<br />
# Pieper, Steve :: Isomics, Inc.<br />
# Pinter, Csaba :: Queen's University<br />
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School<br />
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School<br />
# Rankin, Adam :: Robarts Research Institute<br />
# Rheault, Francois :: Université de Sherbrooke<br />
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin<br />
# Sharp, Gregory :: Massachusetts General Hospital<br />
# Sridharan, Patmaa :: University of Pennsylvania-CBICA<br />
# Szasz, Teodora :: University of Chicago<br />
# Unadkat, Prashin :: Brigham and Women's Hospital<br />
# Van Griethuysen , Joost :: Netherlands Cancer Institute<br />
# Vidyaratne, Lasitha :: Old Dominion University<br />
# Wang, Yaofei :: Brigham and Women's Hospital<br />
# Wang, Ziyang :: Brigham and Women's Hospital<br />
# Wei, Dawei :: Brigham and Women's Hospital<br />
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School<br />
# Xu, Wanxin :: Brigham and Women's Hospital<br />
# Yang, Yixin :: Brigham and Women's Hospital<br />
# Ye, Wu :: Brigham and Women's Hospital<br />
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy<br />
# Zeleznik, Roman :: DFCI<br />
# Zhang, Fan :: Brigham and Women's Hospital<br />
# Zhang, Miaomiao :: Massachusetts Institute of Technology<br />
# Zhang, Yuqian :: Brigham and Women's Hospital<br />
# Zhou, Haoyin :: Brigham and Women's Hospital<br />
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital</div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Project_Week_Template&diff=944642017 Project Week Template2017-01-07T09:11:06Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
</gallery><br />
<br />
==Key Investigators==<br />
<!-- Add a bulleted list of investigators and their institutions here --><br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
* <br />
|<br />
<!-- Approach and Plan bullet points --><br />
* <br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week) --><br />
*<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --></div>Mpc25https://www.na-mic.org/w/index.php?title=2017_Project_Week_Template&diff=944632017 Project Week Template2017-01-07T09:09:49Z<p>Mpc25: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]<br />
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" --><br />
</gallery><br />
<br />
==Key Investigators==<br />
<!-- Add a bulleted list of investigators and their institutions here --> <br />
* Michael Chae (Monash University, Australia) <br />
* Andras Lasso (Queen’s University, Canada) <br />
* Julian Smith (Monash University, Australia) <br />
* Warren Rozen (Monash University, Australia) <br />
* David Hunter-Smith (Monash University, Australia)<br />
<br />
==Project Description==<br />
{| class="wikitable"<br />
! style="text-align: left; width:27%" | Objective<br />
! style="text-align: left; width:27%" | Approach and Plan<br />
! style="text-align: left; width:27%" | Progress and Next Steps<br />
|- style="vertical-align:top;"<br />
|<br />
<!-- Objective bullet points --><br />
* <br />
|<br />
<!-- Approach and Plan bullet points --><br />
* <br />
|<br />
<!-- Progress and Next steps bullet points (fill out at the end of project week) --><br />
*<br />
|}<br />
<br />
==Background and References==<br />
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --></div>Mpc25