Automatic Landmark-Based Segmentation of Human Subcortical Structures in MRI
For neuroimaging researchers, this method improves anatomical consistency of automated segmentations, reducing divergence from expert-defined boundaries.
This paper introduces a landmark-guided 3D segmentation method for subcortical brain structures in MRI that mimics manual protocols, achieving improved boundary accuracy by detecting 16 landmarks and separating 12 coarse labels into 26 distinct structures.
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.