SAM2-3dMed：赋能SAM2进行3D医学图像分割
SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation
AI总结：
针对SAM2直接用于3D医学图像分割时的双向解剖连续性和边界精度不足问题，提出SAM2-3dMed，通过切片相对位置预测和边界检测模块，在MSD数据集上显著超越现有方法。
摘要：
3D医学图像的准确分割对于疾病评估和治疗规划等临床应用至关重要。尽管Segment Anything Model 2（SAM2）通过利用时间线索在视频目标分割中取得了显著成功，但其直接应用于3D医学图像面临两个基本领域差距：1）切片之间的双向解剖连续性与视频中的单向时间流形成鲜明对比；2）对于形态学分析至关重要的精确边界描绘，在视频任务中往往未被充分探索。为弥合这些差距，我们提出了SAM2-3dMed，这是SAM2对3D医学成像的适应性改进。我们的框架引入了两项关键创新：1）切片相对位置预测（SRPP）模块，通过以自监督方式引导SAM2预测不同切片的相对位置，显式建模双向切片间依赖关系；2）边界检测（BD）模块，增强沿关键器官和组织边界的分割精度。在三个多样化医学数据集（医学分割十项全能（MSD）数据集中的肺、脾和胰腺）上进行的大量实验表明，SAM2-3dMed显著优于最先进的方法，在分割重叠和边界精度方面取得了优越性能。我们的方法不仅推进了3D医学图像分割性能，还为将视频中心基础模型适应于空间体积数据提供了一种通用范式。
英文摘要：
Accurate segmentation of 3D medical images is critical for clinical applications like disease assessment and treatment planning. While the Segment Anything Model 2 (SAM2) has shown remarkable success in video object segmentation by leveraging temporal cues, its direct application to 3D medical images faces two fundamental domain gaps: 1) the bidirectional anatomical continuity between slices contrasts sharply with the unidirectional temporal flow in videos, and 2) precise boundary delineation, crucial for morphological analysis, is often underexplored in video tasks. To bridge these gaps, we propose SAM2-3dMed, an adaptation of SAM2 for 3D medical imaging. Our framework introduces two key innovations: 1) a Slice Relative Position Prediction (SRPP) module explicitly models bidirectional inter-slice dependencies by guiding SAM2 to predict the relative positions of different slices in a self-supervised manner; 2) a Boundary Detection (BD) module enhances segmentation accuracy along critical organ and tissue boundaries. Extensive experiments on three diverse medical datasets (the Lung, Spleen, and Pancreas in the Medical Segmentation Decathlon (MSD) dataset) demonstrate that SAM2-3dMed significantly outperforms state-of-the-art methods, achieving superior performance in segmentation overlap and boundary precision. Our approach not only advances 3D medical image segmentation performance but also offers a general paradigm for adapting video-centric foundation models to spatial volumetric data.
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