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Evaluation of AI methods for MRI segmentation on IDC data

Key Investigators

• Cosmin Ciausu (Brigham and Women's Hospital, US)
• Deepa Krishnaswamy (Brigham and Women's Hospital, US)
• Megha Kalia (Brigham and Women's Hospital, US)
• Andrey Fedorov (Brigham and Women's Hospital, US)

Presenter location: In-person

Project Description

We previously studied the application of a contrast-agnostic approach for MRI/CT abdomen organs segmentation, based on the generation of synthetic data. This synthetic data was then further used as a training set for a fully-supervised U-Net network. Since this study was performed, other methods aiming to segment MR abdominal organs have been published. Our goal is to evaluate these new methods on IDC MR abdominal-focused data and see how it compares to our method.

Objective

1. Evaluate performance of MR abdominal organs methods on IDC data.
2. Get feedback on our own method.

Approach and Plan

1. Select a subset of IDC MR abdominal-focused IDC data.
2. Create evaluation notebooks for newly published methods on this subset.
3. Compare to our method.

Progress and Next Steps

1. GitHub repo for in progress colab notebooks

Illustrations

No response

Background and References

• Our method
  • Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
• New published methods
  • MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
  • TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images
  • MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
  • TotalVibeSegmentator: Full Torso Segmentation for the NAKO and UK Biobank in Volumetric Interpolated Breath-hold Examination Body Images