Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters. Code and checkpoints are available at https://github.com/UARK-AICV/SAM3D.

翻译：图像分割仍是医学图像分析中的核心环节，有助于提取关键信息以支持精准诊断实践。随着深度学习的兴起，自动化图像分割方法脱颖而出，在处理医学影像方面展现出卓越性能。受Segment Anything Model（SAM）——这一以分割二维自然图像时具有显著精度与强大泛化能力而闻名的基础模型——启发，我们提出SAM3D，这是一项针对三维体素医学图像分析的创新性适配方案。不同于当前基于SAM的方法将体素数据转换为独立二维切片进行逐片分析，我们的SAM3D模型以统一方式处理整个三维体素图像。我们在多个医学图像数据集上进行了广泛实验，证明本网络在三维医学分割任务中能够获得与现有最先进方法相竞争的结果，同时在参数效率上显著更优。相关代码与检查点可在https://github.com/UARK-AICV/SAM3D获取。