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The following is a collection of electronic resources provided by NA-MIC. This includes software, data, tutorials, presentations, and additional documentation.
 
The following is a collection of electronic resources provided by NA-MIC. This includes software, data, tutorials, presentations, and additional documentation.
  
 
==Software==
 
==Software==
  
* [http://www.slicer.org/pages/Special:SlicerDownloads Download Slicer]
+
{|class=wikitable
* [[NA-MIC-Kit | Download the NA-MIC Kit, including Slicer]]
+
|[[Image:Slicer3_logo-i.jpg]]
 +
|[http://download.slicer.org Download Slicer]<br>
 +
A general purpose biomedical computing application with extensive built-in visualization and analysis capabilities, accessible through an easy to use graphical interface.
 +
|-
 +
|[[Image:NAMIC-Kit-Overview-i.png]]
 +
|[[NA-MIC-Kit | Download the NA-MIC Kit, including Slicer]]<br>
 +
The NA-MIC Kit is a free open source software platform. The NA-MIC Kit is distributed under a BSD-style license without commercial restrictions or "give-back" requirements and is intended for research, but there are no restrictions on other uses. It consists of the 3D Slicer application software, a number of tools and toolkits such as VTK and ITK, and a software engineering methodology that enables multiplatform implementations.
 +
|}
  
 
==Data==
 
==Data==
  
* [http://insight-journal.org/midas/collection/view/190 Brain:  Multi-modality (sMRI, DTI, fMRI)]
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{|class=wikitable
* [http://insight-journal.org/midas/community/view/24 Brain:  2-4 Year Old]
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* [http://insight-journal.org/midas/community/view/25 Prostate:  5 cases]
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|[[Image:Schiz-thumb.png]]
* [http://insight-journal.org/midas/item/view/2454 Prostate:  PerkStation Tutorial Dataset]
+
|[http://insight-journal.org/midas/collection/view/190 Brain:  Multi-modality (sMRI, DTI, fMRI) from Schizophrenia Study]<br>
* [http://insight-journal.org/midas/collection/view/189 ProstateTransrectal Tutorial Dataset]
+
There are 20 cases: ten are Normal Controls and ten are Schizophrenic.  Each case includes a weighted T1 scan, a weighted T2 scan, an fMRI scan, a DTI volume, the DWI with 51 directions, and several masks and labelmaps.  Available from Harvard.
* [https://mri.radiology.uiowa.edu//visible_human_datasets.html Visible Human Datasets]
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|-
* [[Projects:RegistrationDocumentation:UseCaseInventory | Registration Use Case Library]]
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|[[Image:Child-i.png]]
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|[http://insight-journal.org/midas/community/view/24 Brain:  2-4 Year Old from Autism Study]<br>
 +
Data for 2 autistic children and 2 normal controls (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.  Available from UNC.
 +
|-
 +
|[[Image:Lupus-i.png]]
 +
|[http://insight-journal.org/midas/collection/view/191 Brain:  White Matter Lesions for Lupus Study]<br>
 +
Data for 5 cases of Lupus White Matter Lesion patients.  The data is co-registered.  Each case contains:  T1-weighted, T2-weighted, FLAIR, and masks for brain and lesions.  Available from MIND.
 +
|-
 +
|[[Image:Prostate_thumb1.jpg|100px]]
 +
|[http://insight-journal.org/midas/community/view/25 Prostate:  5 robot-assisted intervention cases for Prostate Cancer]<br>
 +
MRI Prostate data. 5 datasets, with pre-operative and intra-operative scans (biopsy and seed placement procedure).  Acquired at National Institute of Health (Principal Investigators: Camphausen, Kaushal and Pinto).
 +
|-
 +
|[[Image:Prostate_thumb2.jpg|100px]]
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|[http://central.xnat.org/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/NCIGT_PROSTATE Prostate:  10 cases]<br>
 +
MRI Prostate data. 10 datasets, including a derived segmentation series with labelmaps.  Available from Harvard.
 +
|-
 +
|[[Image:Trptutorial_thumb.jpg|100px]]
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|[http://insight-journal.org/midas/collection/view/189 Prostate:  Transrectal Tutorial Dataset]<br>
 +
Transrectal Prostate Biopsy Tutorial Dataset.  Walks the user through:  Calibration (calibration image for the APT-MRI device), Segmentation (prostate MRI image and seeds for random walk segmentation algorithm), Targeting (target planning prostate MRI image), and Verification (needle insertion verification image).  Available from Queens.
 +
|-
 +
|[[Image:Perkstation-i.jpg]]
 +
|[http://insight-journal.org/midas/item/view/2454 Spine PhantomPerkStation Tutorial Dataset]<br>
 +
Perkstation Tutorial Dataset.  Available from Queens.
 +
|-
 +
|[[Image:Vizhuman_thumb.png|100px]]
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|[https://mri.radiology.uiowa.edu/visible_human_datasets.html Visible Human Datasets]<br>
 +
Visible Human Datasets with some post-processing.  Available from Iowa.
 +
|-
 +
|[[Image:SlicerRegistrationLibrary-i.png]]
 +
|[[Projects:RegistrationDocumentation:UseCaseInventory | Registration Case Library Home Page]]<br>
 +
New and growing (Oct. 2009 - Sept. 2011) list of image datasets for testing 3DSlicer registration methods & modules.  Images range from brain to abdominal  to musculoskeletal, modalities range from MRI, CT to PET.  Data includes raw image data (NRRD), registration task description & discussion, results, parameter preset files and step-by step tutorials. Built for the clinician researcher to find a related  image registration problem and thus provide a starting point for registration parameters and strategies. 
 +
*[http://www.insight-journal.org/midas/item/view/2332  Registration Library  cases on the MIDAS database]
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|-
 +
|[[Image:CarmaData.png|100px]]
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|[http://www.insight-journal.org/midas/collection/view/197 CARMA late-gadolinium MRI images and segmentations]  <br>
 +
Late-gadolinium enhancement data from the CARMA Center. Sixty anonymized sample datasets are currently available. They consist of pre-RF-ablation images and post-RF-ablation images along with manual segmentations of the left atrial walls, and MRA images as well. For most subjects, two post-RF-ablation images at 3 and 6 month intervals or 4 and 7 month intervals are available. Some subject datasets have post-RF-ablation images at different intervals and may not have MRA data. <br>
 +
[http://www.insight-journal.org/midas/collection/view/197 CARMA Longitudinal Left Atrial Shape Data] <br>
 +
Segmentations of the left atria of sixty anonymized sample datasets with the pulmonary veins and appendage removed. The segmentation data will be used for generating corresponding points and mappings between local and average left atrium shape using ShapeWorks, an open-source software with tools for preprocessing data, computing point-based shape models, and visualizing the results.
 +
|}
  
 
==Tutorials==
 
==Tutorials==
'''Tutorials for Biomedical Engineers and Clinical Research Users of the NA-MIC Kit'''
+
'''Here is a sampling of the available tutorials for Biomedical Engineers and Clinical Research Users of the NA-MIC Kit (PDF and PPT downloads)'''
* [[Media:Stochastic_June09_1.ppt|Python Stochastic Tractography Module]]  
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The full compendium is found [https://www.slicer.org/wiki/Slicer3.6:Training here]
* [[media:Slicer3Training_WhiteMatterLesions_v2.3.pdf‎|White Matter Lesion Classification]]
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{| class="wikitable"
* [[media:DBP2JohnsHopkinsTransRectalProstateBiopsy_TutorialPres2009June.pdf‎|Trans-rectal MR guided prostate biopsy]] and [[Media:PerkStationSlicerTutorial.pdf‎|PerkStationSlicerTutorial]]
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|[[Image:Stochastic-i.png]]
* [[media:ARCTIC-Slicer3-Tutorial.ppt‎|ARCTIC: Automatic Regional Cortical ThICkness]]
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|[[Media:Stochastic_June09_1.ppt|Stochastic Tractography to extract, visualize and quantify white matter connections from Diffusion Images in Schizophrenia Study]]<br>
* [[media:Microscopy-Confocal-TrainingTutorial-2009JUNE.pdf|Confocal Microscopy]]
+
The python stochastic tractography module contains the tools necessary to extract, visualize and quantify white matter connections from DTI images. It seeds nerve fiber bundles from regions of interest (ROIs) based on DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. 
* [[media:EMSegment_TrainingTutorial.pdf| Non-human Primates Segmentation Tutorial]]
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|-
* [[AHM 2010 Tutorial Contest - Hammer Registration | Hammer Registration for Brain MRI]]
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|[[Image:WhiteMatterLesions-i.png]]
* [[AHM 2010 Tutorial Contest - CoronaryArteriesCenterlinesVMTK | Centerline Extraction of Coronary Arteries using VMTK]]
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|[[media:Slicer3Training_WhiteMatterLesions_v2.3.pdf‎|Classification of White Matter Lesions for Lupus]]<br>
* [[AHM 2010 Tutorial Contest - EM Fiber Clustering| EM Fiber Clustering]]
+
This tutorial demonstrates an automated, multi-level method to segment white matter brain lesions in lupus. Following this tutorial, you’ll be able to load scans into Slicer3, and segment and measure the volume of white matter lesions on the provided data-set.
 +
|-
 +
|[[Image:TransRectal-i.png]]
 +
|[[media:DBP2JohnsHopkinsTransRectalProstateBiopsy_TutorialPres2009June.pdf‎|Trans-rectal MR guided prostate biopsy]] and [[Media:PerkStationSlicerTutorial.pdf‎|PerkStationSlicerTutorial]]<br>
 +
This tutorial will teach you how to perform MR-guided prostate biopsy using MR-compatible trans-rectal robot with SLICER.
 +
|-
 +
|[[Image:Arctic-i.png]]
 +
|[[media:ARCTIC-Slicer3-Tutorial.ppt‎|ARCTIC: Automatic Regional Cortical ThICkness Analysis for Autism]]<br>
 +
Following this tutorial, you will be able to perform an individual analysis of regional cortical thickness.  You will learn how to load input volumes, run the end-to-end module ARCTIC to generate cortical thickness information and display output volumes.
 +
|-
 +
|[[Image:Confocal-i.png]]
 +
|[[media:Microscopy-Confocal-TrainingTutorial-2009JUNE.pdf|Confocal Microscopy]]<br>
 +
Guiding you step by step through the process of loading confocal microscopy data, working with that data, and creating a 3D model for visualization.
 +
|-
 +
|[[Image:Primate-i.jpg]]
 +
|[[media:EMSegment_TrainingTutorial.pdf| Non-human Primates Segmentation Tutorial]]<br>
 +
The objective of this tutorial is to demonstrate how to use EM Segmenter to segment non-human primate images.
 +
|-
 +
|[[Image:Hammer-i.png]]
 +
|[[AHM 2010 Tutorial Contest - Hammer Registration | Hammer Registration for Brain MRI]]<br>
 +
Presents HAMMER registration algorithm and introduces how to use HAMMER in Slicer3.
 +
|-
 +
|[[Image:CenterLine-i.png]]
 +
|[[AHM 2010 Tutorial Contest - CoronaryArteriesCenterlinesVMTK | Centerline Extraction of Coronary Arteries using VMTK]]<br>
 +
Guiding you step by step through the process of centerline extraction of Coronary Arteries in a cardiac blood-pool MRI using VMTK based Tools.
 +
|-
 +
|[[File:EMFiberClustering-i.png]]
 +
|[[AHM 2010 Tutorial Contest - EM Fiber Clustering| EM Fiber Clustering]]<br>
 +
This module clusters a set of input trajectories into a number of bundles, generates arc length parameterization by establishing the point correspondences and reports diffusion parameters along the bundles as well as the membership probability of each trajectory in each cluster. The module requires specification of seed trajectories (or initial centerlines) as representatives of the desired bundles.
 +
|}
  
'''Additional Tutorials for Developers'''
+
'''Additional Tutorials'''
  
* [[Slicer3:Training | Slicer3 Training 101]]
+
*[https://www.slicer.org/wiki/Slicer3.6:Training Slicer Training]

Latest revision as of 16:59, 10 July 2017

Home < Downloads

The following is a collection of electronic resources provided by NA-MIC. This includes software, data, tutorials, presentations, and additional documentation.

Software

Slicer3 logo-i.jpg Download Slicer

A general purpose biomedical computing application with extensive built-in visualization and analysis capabilities, accessible through an easy to use graphical interface.

NAMIC-Kit-Overview-i.png Download the NA-MIC Kit, including Slicer

The NA-MIC Kit is a free open source software platform. The NA-MIC Kit is distributed under a BSD-style license without commercial restrictions or "give-back" requirements and is intended for research, but there are no restrictions on other uses. It consists of the 3D Slicer application software, a number of tools and toolkits such as VTK and ITK, and a software engineering methodology that enables multiplatform implementations.

Data

Schiz-thumb.png Brain: Multi-modality (sMRI, DTI, fMRI) from Schizophrenia Study

There are 20 cases: ten are Normal Controls and ten are Schizophrenic. Each case includes a weighted T1 scan, a weighted T2 scan, an fMRI scan, a DTI volume, the DWI with 51 directions, and several masks and labelmaps. Available from Harvard.

Child-i.png Brain: 2-4 Year Old from Autism Study

Data for 2 autistic children and 2 normal controls (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation. Available from UNC.

Lupus-i.png Brain: White Matter Lesions for Lupus Study

Data for 5 cases of Lupus White Matter Lesion patients. The data is co-registered. Each case contains: T1-weighted, T2-weighted, FLAIR, and masks for brain and lesions. Available from MIND.

Prostate thumb1.jpg Prostate: 5 robot-assisted intervention cases for Prostate Cancer

MRI Prostate data. 5 datasets, with pre-operative and intra-operative scans (biopsy and seed placement procedure). Acquired at National Institute of Health (Principal Investigators: Camphausen, Kaushal and Pinto).

Prostate thumb2.jpg Prostate: 10 cases

MRI Prostate data. 10 datasets, including a derived segmentation series with labelmaps. Available from Harvard.

Trptutorial thumb.jpg Prostate: Transrectal Tutorial Dataset

Transrectal Prostate Biopsy Tutorial Dataset. Walks the user through: Calibration (calibration image for the APT-MRI device), Segmentation (prostate MRI image and seeds for random walk segmentation algorithm), Targeting (target planning prostate MRI image), and Verification (needle insertion verification image). Available from Queens.

Perkstation-i.jpg Spine Phantom: PerkStation Tutorial Dataset

Perkstation Tutorial Dataset. Available from Queens.

Vizhuman thumb.png Visible Human Datasets

Visible Human Datasets with some post-processing. Available from Iowa.

SlicerRegistrationLibrary-i.png Registration Case Library Home Page

New and growing (Oct. 2009 - Sept. 2011) list of image datasets for testing 3DSlicer registration methods & modules. Images range from brain to abdominal to musculoskeletal, modalities range from MRI, CT to PET. Data includes raw image data (NRRD), registration task description & discussion, results, parameter preset files and step-by step tutorials. Built for the clinician researcher to find a related image registration problem and thus provide a starting point for registration parameters and strategies.

CarmaData.png CARMA late-gadolinium MRI images and segmentations

Late-gadolinium enhancement data from the CARMA Center. Sixty anonymized sample datasets are currently available. They consist of pre-RF-ablation images and post-RF-ablation images along with manual segmentations of the left atrial walls, and MRA images as well. For most subjects, two post-RF-ablation images at 3 and 6 month intervals or 4 and 7 month intervals are available. Some subject datasets have post-RF-ablation images at different intervals and may not have MRA data.
CARMA Longitudinal Left Atrial Shape Data
Segmentations of the left atria of sixty anonymized sample datasets with the pulmonary veins and appendage removed. The segmentation data will be used for generating corresponding points and mappings between local and average left atrium shape using ShapeWorks, an open-source software with tools for preprocessing data, computing point-based shape models, and visualizing the results.

Tutorials

Here is a sampling of the available tutorials for Biomedical Engineers and Clinical Research Users of the NA-MIC Kit (PDF and PPT downloads) The full compendium is found here

Stochastic-i.png Stochastic Tractography to extract, visualize and quantify white matter connections from Diffusion Images in Schizophrenia Study

The python stochastic tractography module contains the tools necessary to extract, visualize and quantify white matter connections from DTI images. It seeds nerve fiber bundles from regions of interest (ROIs) based on DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography.

WhiteMatterLesions-i.png Classification of White Matter Lesions for Lupus

This tutorial demonstrates an automated, multi-level method to segment white matter brain lesions in lupus. Following this tutorial, you’ll be able to load scans into Slicer3, and segment and measure the volume of white matter lesions on the provided data-set.

TransRectal-i.png Trans-rectal MR guided prostate biopsy and PerkStationSlicerTutorial

This tutorial will teach you how to perform MR-guided prostate biopsy using MR-compatible trans-rectal robot with SLICER.

Arctic-i.png ARCTIC: Automatic Regional Cortical ThICkness Analysis for Autism

Following this tutorial, you will be able to perform an individual analysis of regional cortical thickness. You will learn how to load input volumes, run the end-to-end module ARCTIC to generate cortical thickness information and display output volumes.

Confocal-i.png Confocal Microscopy

Guiding you step by step through the process of loading confocal microscopy data, working with that data, and creating a 3D model for visualization.

Primate-i.jpg Non-human Primates Segmentation Tutorial

The objective of this tutorial is to demonstrate how to use EM Segmenter to segment non-human primate images.

Hammer-i.png Hammer Registration for Brain MRI

Presents HAMMER registration algorithm and introduces how to use HAMMER in Slicer3.

CenterLine-i.png Centerline Extraction of Coronary Arteries using VMTK

Guiding you step by step through the process of centerline extraction of Coronary Arteries in a cardiac blood-pool MRI using VMTK based Tools.

EMFiberClustering-i.png EM Fiber Clustering

This module clusters a set of input trajectories into a number of bundles, generates arc length parameterization by establishing the point correspondences and reports diffusion parameters along the bundles as well as the membership probability of each trajectory in each cluster. The module requires specification of seed trajectories (or initial centerlines) as representatives of the desired bundles.

Additional Tutorials