Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks
نویسندگان
چکیده
Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, performed on T 1 -weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report a comparative study automated, learning-based various other contrasts also computed tomography (CT) investigate anatomical information contained in these imaging modalities. A large database total 853 MRI/CT enables us train convolutional neural networks (CNNs) segmentation. We benchmark CNN performance four different modalities 27 substructures. For each modality separate based common architecture. find average Dice scores 86.7 ± 4.1% ( MRI), 81.9 6.7% (fluid-attenuated inversion recovery 80.8 6.6% (diffusion-weighted MRI) 80.7 8.2% (CT), respectively. The assessed relative labels obtained using widely-adopted FreeSurfer software package. pipeline uses dropout sampling identify corrupted input or low-quality segmentations. Full 3D volumes with more than 2 million voxels requires <1s processing time graphical unit.
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ژورنال
عنوان ژورنال: Frontiers in Neurology
سال: 2021
ISSN: ['1664-2295']
DOI: https://doi.org/10.3389/fneur.2021.653375