Mathijs de Boer, Tessa M Kos, Tim Fick, Jesse A M van Doormaal, Elisa Colombo, Hugo J Kuijf, Pierre A J T Robe, Luca P Regli, Lambertus W Bartels, Tristan P C van Doormaal
PURPOSE: This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). METHODS: We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared...
February 20, 2024: Acta Neurochirurgica