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

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 3D segmentation. Results: The MultiAxial network achieved a test-set Dice scores of Conclusions: We are releasing a new state-of-the-art tool for whole-head MRI segmentation in abnormal anatomy, along with the largest volume of labeled clinical head MRIs, including labels for nonbrain structures. Together, the model and data may serve as a benchmark for future efforts.