Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images
Year: 2025
Type: article
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.
Source: npj Digital Medicine
Authors: Yuhan Zhang, Xiao Ma, Mingchao Li, Kun Huang, Jie Zhu, Miao Wang, Xi Wang, Menglin Wu, Pheng-Ann Heng
Institutions: Chinese University of Hong Kong, Shenzhen University, Nanjing University of Science and Technology, Imperial College London, Tsinghua University, Chinese PLA General Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Hospital, Hong Kong University of Science and Technology, Nanjing Tech University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,090
OpenAlex ID: W4411433420