用关键点引导分割，让脑部结构识别更贴近专家标准。
Automatic Landmark-Based Segmentation of Human Subcortical Structures in MRI
- 通过16个关键点定位，指导3D分割流程
- 边界精度显著提升，26个结构分离更准确
- 适合需要高解剖一致性医学影像分析的场景
精准的脑部结构分割对神经影像分析至关重要，但传统的体素级深度模型常产生与专家定义边界不符的结果。本文提出一种基于地标引导的3D脑部分割方法，模拟哈佛-牛津图谱的手动分割流程。首先，全局到局部网络自动检测16个代表关键亚皮层参考点的地标；接着，语义分割模型生成12个解剖标签的粗略分割，每标签包含多个亚皮层区域；最后，通过基于地标的后处理步骤，依据局部解剖约束将12个标签细分为26个独立结构。实验表明，边界精度显著提升，融合学习到的地標使分割结果更贴合人工协议。
原文摘要 · Abstract (English)
Precise segmentation of brain structures in magnetic resonance imaging (MRI) is essential for reliable neuroimaging analysis, yet voxel-wise deep models often yield anatomically inconsistent results that diverge from expert-defined boundaries. In this research, we propose a landmark-guided 3D brain segmentation approach that explicitly mimics the manual segmentation protocol of the Harvard--Oxford Atlas. A Global-to-Local network automatically detects 16 landmarks representing key subcortical reference points. Then, a semantic segmentation model produces a coarse segmentation of 12 anatomical labels, each grouping multiple subcortical regions. Finally, a landmark-driven post-processing step separates these 12 labels into 26 distinct structures by enforcing local anatomical constraints. Experimental results demonstrate consistent improvements in boundary accuracy. Overall, integrating learned landmarks aligns segmentations more closely with manual protocols.
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