Project: Review of segmentation results quality across various multi-organ segmentation models

[fedorov]

Draft Status

Ready - team will start page creating immediately

Category

Segmentation / Classification / Landmarking

Key Investigators

• Andrey Fedorov (BWH, USA)
• Ron Kikinis (BWH, USA)
• David Clunie (Pixelmed Publishing, USA)
• Steve Pieper (Isomics Inc, USA)

Project Description

When initially released, TotalSegmentator was perceived to produce superior results, in comparison to the state-of-the-art at the time, anyway.

Over time, some of the deficiencies in the segmentations produced by TotalSegmentator have been identified. Further, new multi-organ segmentation models have been introduced.

Objective

1. Review segmentation results for a sample of images from IDC NLST collection, documenting the problems, across the publicly available multi-organ segmentation models.

Approach and Plan

For any model to be evaluated, we need to have segmentation results available for the specific set of CT images from NLST.

At this moment, we have those for

• TotalSegmentator v1
• MOOSE

We are hopeful to also get segmentations for those selected specific cases for the following (given the list of participants in PW):

• OMAS (@aTamaz will you help?)
• AutoSeg3D (@diazandr3s can you help?)

We plan to use the SegmentationVerification extension developed by @cpinter in the PW41 earlier for the review (see https://projectweek.na-mic.org/PW41_2024_MIT/Projects/SegmentationVerificationModuleForFinalizingMultiLabelAiSegmentations/).

We plan to summarize the results of the review in a publicly available document.

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

No response

Background and References

No response