4 papers
Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
Guoping Xu, Jayaram K. Udupa, Yubing Tong +5
Accurate lesion segmentation is essential in medical image analysis, yet most existing methods are designed for specific anatomical sites or imaging modalities, limiting their gene…
Exploiting DINOv3-Based Self-Supervised Features for Robust Few-Shot Medical Image Segmentation
Guoping Xu, Jayaram K. Udupa, Weiguo Lu +1
Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the…
SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2
Guoping Xu, Yan Dai, Hengrui Zhao +4
Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for…
A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging
Guoping Xu, Xiaoxue Qian, Hua Chieh Shao +3
This study introduces SAMatch, a SAM-guided Match-based framework for semi-supervised medical image segmentation, aimed at improving pseudo label quality in data-scarce scenarios.…