The paper "Foundation Models for Biomedical Image Segmentation: A Survey" by Ho Hin Lee, Yu Gu, Theodore Zhao, Yanbo Xu, Jianwei Yang, Naoto Usuyama, Cliff Wong, Mu Wei, Bennett A. Landman, Yuankai Huo, Alberto Santamaria-Pang, and Hoifung Poon, published by Microsoft Research and Vanderbilt University, provides an in-depth review of the Segment Anything Model (SAM) and its applications in biomedical image segmentation. The authors focus on the period from April 1, 2023, to September 30, 2023, a critical six-month period following the initial publication of SAM. They analyze 33 open datasets to assess SAM's performance and explore its adaptations to address clinical challenges. SAM, originally developed for general-purpose computer vision, has shown remarkable potential in medical image processing, particularly in zero-shot learning. The model's ability to segment objects without prior knowledge of their type or imaging modality aligns well with the human visual system's adaptability. However, its application in non-biological vision contexts remains theoretically challenging. The paper highlights SAM's adaptability to different resolutions and areas of interest, similar to semantic priming, which has spurred innovation in medical imaging. Despite achieving state-of-the-art performance in many applications, SAM faces limitations in segmenting complex anatomical regions such as the carotid artery, adrenal glands, optic nerve, and mandible bone. The authors delve into the innovative techniques where SAM excels and explore the core concepts for effective translation and application in various medical imaging scenarios. They also discuss the challenges in contemporary medical image segmentation tasks, including versatile medical image modalities and fine-grained segmentation tasks. The paper categorizes SAM's adaptation into four primary methodologies: zero-shot evaluation, domain-specific tuning (including projection tuning, adapter tuning, and full tuning), 3D imaging modality extension, and knowledge distillation. Each methodology is evaluated based on its effectiveness in addressing the unique challenges of medical imaging. Finally, the authors discuss the limitations of current SAM adaptation approaches, such as generalization discrepancy, fine-tuning dilemmas, and modality inconsistencies. They also highlight the specific requirements of medical image segmentation, including the incorporation of metadata and population analysis. The paper concludes with a discussion on prospective horizons, emphasizing the potential for segmenting unseen classes and enhancing explainable interpretability in medical image segmentation using SAM.The paper "Foundation Models for Biomedical Image Segmentation: A Survey" by Ho Hin Lee, Yu Gu, Theodore Zhao, Yanbo Xu, Jianwei Yang, Naoto Usuyama, Cliff Wong, Mu Wei, Bennett A. Landman, Yuankai Huo, Alberto Santamaria-Pang, and Hoifung Poon, published by Microsoft Research and Vanderbilt University, provides an in-depth review of the Segment Anything Model (SAM) and its applications in biomedical image segmentation. The authors focus on the period from April 1, 2023, to September 30, 2023, a critical six-month period following the initial publication of SAM. They analyze 33 open datasets to assess SAM's performance and explore its adaptations to address clinical challenges. SAM, originally developed for general-purpose computer vision, has shown remarkable potential in medical image processing, particularly in zero-shot learning. The model's ability to segment objects without prior knowledge of their type or imaging modality aligns well with the human visual system's adaptability. However, its application in non-biological vision contexts remains theoretically challenging. The paper highlights SAM's adaptability to different resolutions and areas of interest, similar to semantic priming, which has spurred innovation in medical imaging. Despite achieving state-of-the-art performance in many applications, SAM faces limitations in segmenting complex anatomical regions such as the carotid artery, adrenal glands, optic nerve, and mandible bone. The authors delve into the innovative techniques where SAM excels and explore the core concepts for effective translation and application in various medical imaging scenarios. They also discuss the challenges in contemporary medical image segmentation tasks, including versatile medical image modalities and fine-grained segmentation tasks. The paper categorizes SAM's adaptation into four primary methodologies: zero-shot evaluation, domain-specific tuning (including projection tuning, adapter tuning, and full tuning), 3D imaging modality extension, and knowledge distillation. Each methodology is evaluated based on its effectiveness in addressing the unique challenges of medical imaging. Finally, the authors discuss the limitations of current SAM adaptation approaches, such as generalization discrepancy, fine-tuning dilemmas, and modality inconsistencies. They also highlight the specific requirements of medical image segmentation, including the incorporation of metadata and population analysis. The paper concludes with a discussion on prospective horizons, emphasizing the potential for segmenting unseen classes and enhancing explainable interpretability in medical image segmentation using SAM.