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DICOM volume reconstruction tools

Key Investigators

Project Description

Objective

A common task in medical image analysis is to convert the DICOM image series into a 3d volume that is suitable for the analysis using ITK, deep learning studies, etc. The goal of this project is to summarize, discuss, and evaluate various publicly available tools that exist for this task.

Initially, we want to focus on the very narrow task of converting a scalar volume image series.

Approach and Plan

  1. Identify the list of requirements: what kind of data should be acceptable
  2. Compile the list of tools suitable for this task. Include main features of the tool: language, license, support/history of development, pros and cons, …
  3. Collect feedback from the community - what tool do you use? what issues have you encountered? do you feel like you have a solution that is working reliably?
  4. Compile a list of representative datasets, discuss approaches that could be used to evaluate a tool.

Progress and Next Steps

Repository: https://github.com/QIICR/dcmheat

Discussion

Questions from @lassoan:

Tools

3D Slicer DICOM ScalarVolumePlugin

https://github.com/Slicer/Slicer/blob/master/Modules/Scripted/DICOMPlugins/DICOMScalarVolumePlugin.py

dcm2niix

https://github.com/rordenlab/dcm2niix

dicom2nifti

https://github.com/icometrix/dicom2nifti

dcmstack

https://github.com/moloney/dcmstack

vtk-dicom/dicomtools/dicomtonifti

https://github.com/dgobbi/vtk-dicom

FreeSurfer/mri_convert

https://surfer.nmr.mgh.harvard.edu/fswiki/mri_convert

Plastimatch/convert

http://plastimatch.org/plastimatch.html

mriconvert and mcverter

https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Illustrations

A mockup of how the public-facing page could look: https://andrewbeers.github.io/dicomCompare/

Background and References