Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images.
Authors
Affiliations (13)
Affiliations (13)
- School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
- Bioengineering Department and Imperial-X, Imperial College London, London, UK.
- Academy of Arts and Design, Tsinghua University, Beijing, China.
- Senior Department of Urology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China. [email protected].
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. [email protected].
- Carbon Medical Device Ltd., Shenzhen, China. [email protected].
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China. [email protected].
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China. [email protected].
- Institute of Medical Intelligence and XR, The Chinese University of Hong Kong, Hong Kong, China. [email protected].
Abstract
Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3-8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.