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NA-MIC Project Weeks

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Using automatic AI segmentation tools for Imaging Data Commons data enrichment

Key Investigators

Project Description

This project builds on work carried out during PW43 and on the work “In Search of Truth: Evaluating Concordance of AI-Based Anatomy Segmentation Models”.

Our overall goal is to enrich images available in Imaging Data Commons with segmentations and quantitative features.

In this work, we developed a practical workflow to compare AI-based anatomy segmentation models in the absence of ground truth annotations. Segmentation outputs from different models were harmonized into a standardized representation, enabling structure-wise comparison and efficient visual review. Using this framework, we evaluated six open-source segmentation models, TotalSegmentator 1.5, TotalSegmentator 2.6, Auto3DSeg, MOOSE, MultiTalent, and CADS, on 18 CT scans from the NLST dataset hosted by the Imaging Data Commons. While agreement varied across anatomical structures, MOOSE and CADS showed consistent results across all evaluated structures and did not show visible segmentation errors during visual comparison. In contrast, the other four models produced visible segmentation errors or deficiencies in rib and vertebrae structures.

The goal of this Project Week is to select a representative subset of the NLST dataset, run the MOOSE segmentation model on it, and use radiomic features to identify and visually inspect potential segmentation outliers to confirm robustness of the model. Stretch goal is to process all of the CTs in NLST (or even beyond NLST) with MOOSE to generate segmentations and radiomics features, for the subsequent ingestion into Imaging Data Commons.

In addition, the 3DSlicer CrossSegmentationExplorer extension described in the preprint should be finished and published as an extension for 3D Slicer.

Objective

  1. Evaluate how the MOOSE segmentation model performs on a representative subset of the NLST dataset, as the previously analyzed subset did not capture all relevant dataset characteristics.
  2. Publish the CrossSegmentationExplorer extension in 3DSlicer
  3. Identify any new segmentation models known in the community that might be suitable for automatic segmentation tasks.

Approach and Plan

  1. Define criteria for NLST subset selection
  2. Run MOOSE on that subset and extract radiomic features
  3. Analyze feature distributions to detect outliers
  4. Visually review outlier cases in Slicer

Progress and Next Steps

  1. Describe specific steps you have actually done.

Illustrations

No response

Background and References