Full-head segmentation of MRI with abnormal brain anatomy: model and data release.

ABSTRACT:

Purpose

Our goal was to develop a deep network for whole-head segmentation, including clinical magnetic resonance imaging (MRI) with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 98 MRIs with volumetric segmentation labels for a diverse set of human subjects, including normal and abnormal anatomy in clinical cases of stroke and disorders of consciousness.

Approach

Training labels were generated by manually correcting initial automated segmentations for skin/scalp, skull, cerebro-spinal fluid, gray matter, white matter, air cavity, and extracephalic air. We developed a "MultiAxial" network consisting of three 2D U-Net that operate independently in sagittal, axial, and coronal planes, which are then combined to produce a single

SUBMITTER: Birnbaum AM 

PROVIDER: S-EPMC12442731 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature