Researchers introduce an open-source approach for evaluating AI anatomy segmentation models in medical imaging without requiring ground truth annotations.
Key Details
- 1Six open-source AI segmentation models evaluated on chest CT scans from the National Lung Screening Trial (NLST).
- 2All model outputs standardized using DICOM format and harmonized medical lexicons (SNOMED-CV) for fair comparison.
- 3Strong agreement found across models for lung segmentation, but inconsistencies and systematic errors for bones and cardiac structures.
- 4Interactive visualization tools built on OHIF Viewer and 3D Slicer plugin enabled detailed side-by-side model review.
- 5Framework and datasets are open source; enables identification of reliable models and flags problematic cases.
Why It Matters
This framework addresses a major barrier for the radiology AI field by enabling fair, evidence-based model comparison in settings lacking expert-annotated ground truth. Such approaches help researchers select appropriate AI tools for large-scale studies and highlight the variability between widely used segmentation models.
Source
EurekAlert
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