Automatic Landmark-Based Segmentation of Human Subcortical Structures in MRI

TL;DR

This paper introduces a landmark-guided 3D MRI segmentation method that mimics manual protocols, improving boundary accuracy of subcortical structures by integrating landmark detection and local anatomical constraints.

Contribution

The novel approach combines landmark detection with semantic segmentation and post-processing to produce more anatomically consistent brain structure segmentations.

Findings

Improved boundary accuracy over baseline models.

Landmark integration enhances segmentation consistency with manual protocols.

Achieved more anatomically accurate subcortical segmentation results.

Abstract

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.