Part-aware Prompted Segment Anything Model for Adaptive Segmentation
Department of Computer Science and Engineering, University of Michigan（计算机科学与工程系，密歇根大学）；Department of Electrical and Computer Engineering, University of Michigan（电气与计算机工程系，密歇根大学）
Precision medicine, such as patient-adaptive treatments assisted by medical image analysis, poses new challenges for segmentation algorithms in adapting to new patients, due to the large variability across different patients and the limited availability of annotated data for each patient. In this work, we propose a data-efficient segmentation algorithm, namely Part-aware Prompted Segment Anything Model ($P^2SAM$). Without any model fine-tuning, $P^2SAM$ enables seamless adaptation to any new patients relying only on one-shot patient-specific data. We introduce a novel part-aware prompt mechanism to select multiple-point prompts based on the part-level features of the one-shot data, which can be extensively integrated into different promptable segmentation models, such as SAM and SAM 2. Moreover, to determine the optimal number of parts for each specific case, we propose a distribution-guided retrieval approach that further enhances the robustness of the part-aware prompt mechanism. $P^2SAM$ improves the performance by +8.0% and +2.0% mean Dice score for two different patient-adaptive segmentation applications, respectively. In addition, $P^2SAM$ also exhibits impressive generalizability in other adaptive segmentation tasks in the natural image domain, e.g., +6.4% mIoU within personalized object segmentation task. The code is available at: https://github.com/Zch0414/p2sam
Encoding Structural Constraints into Segment Anything Models via Probabilistic Graphical Models
The George Washington University（乔治·华盛顿大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Segment Anything in Medical Images and Videos: Benchmark and Deployment
Peter Munk Cardiac Centre, University Health Network（彼得·芒克心脏中心，大学健康网络）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；AI Hub, University Health Network（人工智能中心，大学健康网络）；University of Toronto（多伦多大学）
本文对 SAM2 在 11 种医学图像模态和视频上进行了全面基准测试，并通过微调迁移学习及 3D Slicer 插件和 Gradio API 部署，验证了其快速适应医学领域并实现高效 3D 分割的能力。
SAM3D: Segment Anything Model in Volumetric Medical Images
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）；University of Arkansas for Medical Sciences（阿肯色医科大学）
MedSAM3: Delving into Segment Anything with Medical Concepts
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Southeast University（东南大学）；The Hong Kong University of Science and Technology（香港科技大学）
Segment Anything Model (SAM) for Radiation Oncology
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Nanjing University of Science and Technology（南京理工大学）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；The Chinese University of Hong Kong（香港中文大学）；University of Notre Dame（圣母大学）
ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
Department of Artificial Intelligence, Sungkyunkwan University, Republic of Korea（人工智能系，成均馆大学，大韩民国）；Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea（放射肿瘤科，三星医疗中心，成均馆大学医学院，大韩民国）；Department of MetaBioHealth, Sungkyunkwan University, Republic of Korea（元生物健康系，成均馆大学，大韩民国）
SPDA-SAM: A Self-prompted Depth-Aware Segment Anything Model for Instance Segmentation
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和中国海洋大学信息科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）
Polyp-DAM: Polyp segmentation via depth anything model
Nanjing University of Science and Technology（南京理工大学）；University of Science and Technology of China（中国科学技术大学）；Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Wuhan University（武汉大学）；Dalian University of Technology（大连理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
