1 citations · 1 across the 8 of their papers we have counts for
8 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…
Is the medical image segmentation problem solved? A survey of current developments and future directions
Guoping Xu, Jayaram K. Udupa, Jax Luo +8
Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues,…
TSMS-SAM2: Multi-scale Temporal Sampling Augmentation and Memory-Splitting Pruning for Promptable Video Object Segmentation and Tracking in Surgical Scenarios
Guoping Xu, Hua-Chieh Shao, You Zhang
Promptable video object segmentation and tracking (VOST) has seen significant advances with the emergence of foundation models like Segment Anything Model 2 (SAM2); however, their…
Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future
Guoping Xu, Jayaram K. Udupa, Yajun Yu +4
Video Object Segmentation and Tracking (VOST) presents a complex yet critical challenge in computer vision, requiring robust integration of segmentation and tracking across tempora…
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…
Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2
Guoping Xu, Christopher Kabat, You Zhang
Recent advances in medical image segmentation have been driven by deep learning; however, most existing methods remain limited by modality-specific designs and exhibit poor adaptab…
SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
Jianghao Wu, Yicheng Wu, Yutong Xie +7
Leveraging the Segment Anything Model (SAM) for medical image segmentation remains challenging due to its limited adaptability across diverse medical domains. Although fine-tuned v…