Brain tumor segmentation presents a formidable challenge in the field of Medical Image Segmentation. While deep-learning models have been useful, human expert segmentation remains the most accurate method. The recently released Segment Anything Model (SAM) has opened up the opportunity to apply foundation models to this difficult task. However, SAM was primarily trained on diverse natural images. This makes applying SAM to biomedical segmentation, such as brain tumors with less defined boundaries, challenging. In this paper, we enhanced SAM's mask decoder using transfer learning with the Decathlon brain tumor dataset. We developed three methods to encapsulate the four-dimensional data into three dimensions for SAM. An on-the-fly data augmentation approach has been used with a combination of rotations and elastic deformations to increase the size of the training dataset. Two key metrics: the Dice Similarity Coefficient (DSC) and the Hausdorff Distance 95th Percentile (HD95), have been applied to assess the performance of our segmentation models. These metrics provided valuable insights into the quality of the segmentation results. In our evaluation, we compared this improved model to two benchmarks: the pretrained SAM and the widely used model, nnUNetv2. We find that the improved SAM shows considerable improvement over the pretrained SAM, while nnUNetv2 outperformed the improved SAM in terms of overall segmentation accuracy. Nevertheless, the improved SAM demonstrated slightly more consistent results than nnUNetv2, especially on challenging cases that can lead to larger Hausdorff distances. In the future, more advanced techniques can be applied in order to further improve the performance of SAM on brain tumor segmentation.
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）
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
Florida Atlantic University（佛罗里达大西洋大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Missouri University of Science and Technology（密苏里科技大学）；Florida International University（佛罗里达国际大学）
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations
Nile University（尼罗大学）；Friedrich-Alexander-Universität（埃尔朗根-纽伦堡大学）；Makerere University（马凯雷雷大学）；Malawi University of Science and Technology（马拉维科技大学）；Medical Artificial Intelligence Laboratory (MAI Lab)（医学人工智能实验室（MAI Lab））；Lawson Health Research Institute（劳森健康研究院）；University of British Columbia（不列颠哥伦比亚大学）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）；University of Cape Town（开普敦大学）
MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI
Institute for Applied Medical Informatics, Institute of Computational Neuroscience, and Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf（应用医学信息学研究所、计算神经科学研究所和生物医学人工智能中心（bAIome）、汉堡医学院中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf（诊断与介入放射学及核医学系、汉堡医学院中心）；Department of Neurology, University Medical Center Hamburg-Eppendorf（神经病学系、汉堡医学院中心）
MOIS-SAM2是一种基于示例的Segment Anything Model 2，用于全身体部MRI中神经纤维瘤多病灶交互分割，实现了高精度和可扩展性，支持临床应用。
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
Carnegie Vanguard High School（卡内基先锋高中）；Devon Preparatory High School（德文预备高中）；Lake Travis High School（特拉维斯湖高中）；Irvington High School（欧文顿高中）；Algoverse AI Research（Algoverse AI研究机构）
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）
Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model
Department of Nuclear Medicine, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University（核医学科，上海儿童医学中心，上海交通大学医学院）；School of Microelectronics, Shanghai University（微电子学院，上海大学）；Department of Ophthalmology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（眼科，瑞金医院，上海交通大学医学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Software Engineering, Sun Yat-sen University（软件工程学院，中山大学）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；School of Computer Science and Technology, Henan Polytechnic University（计算机科学与技术学院，河南理工大学）
本文首次将 SAM 语义先验引入医学图像恢复，通过级联网络和三个语义模块增强儿科快速骨闪烁显像，并构建首个专用数据集 RBS，在多项指标上优于现有方法。
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）
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。
Xidian University（西安电子科技大学）；Weill Cornell Medical School, Cornell University（康奈尔大学威尔康奈尔医学院）
This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct many skip connections to link features at the same resolution and transmit detailed information from shallow layers to the deeper layers. Then…
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）
