Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient variants reduce computation but still depend on large-scale training. We propose a lightweight RGB-D fusion framework that augments EfficientViT-SAM with monocular depth priors. Depth maps are generated with a pretrained estimator and fused mid-level with RGB features through a dedicated depth encoder. Trained on only 11.2k samples (less than 0.1\% of SA-1B), our method achieves higher accuracy than EfficientViT-SAM, showing that depth cues provide strong geometric priors for segmentation.
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）
Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院暨研究所）；Nanjing University（南京大学）
Zhejiang University（浙江大学）；Westlake University（西湖大学）；University of Electronic Science and Technology of China（电子科技大学）；Pennsylvania State University（宾夕法尼亚州立大学）
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releas…
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）
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；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）；Shanghai Jiaotong University（上海交通大学）；the Chinese University of Hong Kong（香港中文大学）
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）
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）
