NA-MIC Project Weeks

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Automatic segmentation of priority collections from Imaging Data Commons

Key Investigators

• Kyle Sunderland (Queen's, Canada)
• Vamsi Thiriveedhi (MGH, USA)
• Paolo Zaffino (Magna Graecia U. of Catanzaro, Italy)
• Lalith Kumar Shiyam Sundar (LMU, Germany)
• Michael Onken (OpenConnections GmbH, Germany)
• Andrey Fedorov (BWH, USA)

Project Description

The goal of this project is to further increase availability of anatomic segmentations and accompanying radiomics features for the images available in Imaging Data Commons.

Objective

1. Volumetric segmentations of the anatomy for selected images in IDC.
2. Radiomics features for the segmented labels.
3. Interface to explore the resulting data.

Approach and Plan

1. Develop Terra workflow wrapping selected MOOSE, VIBESegmenator and TotalSegmentator models
2. Extract radiomics features using Radiomics.jl
3. Saving resulting segmenations and radiomics features into DICOM using dcmqi
4. Test on a representative sample from IDC (prioritize processing of Cancer Moonshot Biobank and CPTAC images)
5. Apply to a larger cohort
6. Develop review interface, examine results, investigate possible issues
7. Apply workflow to the APOLLO5 dataset (embargoed)

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

Background and References

• https://portal.imaging.datacommons.cancer.gov/
• https://terra.bio/
• https://github.com/ENHANCE-PET/MOOSE
• https://github.com/robert-graf/VIBESegmentator
• https://github.com/pzaffino/Radiomics.jl
• https://github.com/QIICR/dcmqi
• https://github.com/wasserth/totalsegmentator
• https://doi.org/10.1117/1.JMI.13.6.062204