Recent advancements in biomedical image analysis have been significantly
driven by the Segment Anything Model (SAM). This transformative technology,
originally developed for general-purpose computer vision, has found rapid
application in medical image processing. Within the last year, marked by over
100 publications, SAM has demonstrated its prowess in zero-shot learning
adaptations for medical imaging. The fundamental premise of SAM lies in its
capability to segment or identify objects in images without prior knowledge of
the object type or imaging modality. This approach aligns well with tasks
achievable by the human visual system, though its application in non-biological
vision contexts remains more theoretically challenging. A notable feature of
SAM is its ability to adjust segmentation according to a specified resolution
scale or area of interest, akin to semantic priming. This adaptability has
spurred a wave of creativity and innovation in applying SAM to medical imaging.
Our review focuses on the period from April 1, 2023, to September 30, 2023, a
critical first six months post-initial publication. We examine the adaptations
and integrations of SAM necessary to address longstanding clinical challenges,
particularly in the context of 33 open datasets covered in our analysis. While
SAM approaches or achieves state-of-the-art performance in numerous
applications, it falls short in certain areas, such as segmentation of the
carotid artery, adrenal glands, optic nerve, and mandible bone. Our survey
delves into the innovative techniques where SAM's foundational approach excels
and explores the core concepts in translating and applying these models
effectively in diverse medical imaging scenarios.

最近许多生物医学影像分析领域的进展主要受到 Segment Anything Model (SAM) 的推动。这项先进的技术最初是为了通用计算机视觉而开发的，但在医学图像处理领域得到了迅速应用。我们的综述聚焦于 2023 年 4 月 1 日至 9 月 30 日这段重要的首次出版后六个月的时期，我们研究了 SAM 在解决长期临床挑战方面所需的改进和整合，特别关注了我们分析的 33 个开放数据集。尽管 SAM 在许多应用中达到了或超越了最先进的性能水平，但在一些方面仍存在不足，例如对颈动脉、肾上腺、视神经和下颌骨的分割。我们的调查深入研究了 SAM 的创新技术和其在各种医学影像场景中有效转化和应用的核心概念。