Evaluation of effectiveness and fairness of foundation models in multi-organ segmentation

Motivation: Foundation models have significant potential in organ segmentation. However, They would face challenges regards effectiveness and fairness. Goal(s): This study aims to evaluate the effectiveness and fairness of foundation models in multi-organ segmentation. Approach: Foundation models (SAT, SAM, MedSAM, and TotalSegmentator) were included in this study. Bounding box prompts were used with SAM and MedSAM. DICE was used to evaluate segmentation effectiveness. For fairness evaluation, the DICE was grouped by gender, age, and BMI, and ANOVA was used to compute significance between groups. Results: The effectiveness of SAM and MedSAM is more significant than other mdoels. However, their fairness needs to be improved. Impact: This study systematically evaluates the variations in segmentation effectiveness of foundation models across different organs and the fairness issues, which finds the shortcomings of the current foundation models and plays an important role in guiding future improvements of foundation models.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4009
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
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article

Evaluation of effectiveness and fairness of foundation models in multi-organ segmentation

Qirong Li, Yajing Zhang, Xumei Hu, Mengting Sun et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Medical Image Segmentation Techniques
article

Evaluation of effectiveness and fairness of foundation models in multi-organ segmentation

Qirong Li, Yajing Zhang, Xumei Hu, Mengting Sun, Chengyan Wang, Shuo Wang, Yan Li, Meng Liu, Qing Li, Yizhe Zhang, Zian Wang, Longyu Sun
article en

Abstract

Motivation: Foundation models have significant potential in organ segmentation. However, They would face challenges regards effectiveness and fairness. Goal(s): This study aims to evaluate the effectiveness and fairness of foundation models in multi-organ segmentation. Approach: Foundation models (SAT, SAM, MedSAM, and TotalSegmentator) were included in this study. Bounding box prompts were used with SAM and MedSAM. DICE was used to evaluate segmentation effectiveness. For fairness evaluation, the DICE was grouped by gender, age, and BMI, and ANOVA was used to compute significance between groups. Results: The effectiveness of SAM and MedSAM is more significant than other mdoels. However, their fairness needs to be improved. Impact: This study systematically evaluates the variations in segmentation effectiveness of foundation models across different organs and the fairness issues, which finds the shortcomings of the current foundation models and plays an important role in guiding future improvements of foundation models.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Medical Image Segmentation Techniques
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Evaluation of effectiveness and fairness of foundation models in multi-organ segmentation — Qirong Li, Yajing Zhang, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS