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Multi-modal segmentation of 3D brain scans using neural networks

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Document pages: 17 pages

Abstract: Purpose: To implement a brain segmentation pipeline based on convolutionalneural networks, which rapidly segments 3D volumes into 27 anatomicalstructures. To provide an extensive, comparative study of segmentationperformance on various contrasts of magnetic resonance imaging (MRI) andcomputed tomography (CT) scans. Methods: Deep convolutional neural networks aretrained to segment 3D MRI (MPRAGE, DWI, FLAIR) and CT scans. A large databaseof in total 851 MRI CT scans is used for neural network training. Traininglabels are obtained on the MPRAGE contrast and coregistered to the otherimaging modalities. The segmentation quality is quantified using the Dicemetric for a total of 27 anatomical structures. Dropout sampling is implementedto identify corrupted input scans or low-quality segmentations. Fullsegmentation of 3D volumes with more than 2 million voxels is obtained in lessthan 1s of processing time on a graphical processing unit. Results: The bestaverage Dice score is found on $T 1$-weighted MPRAGE ($85.3 pm4.6 , $).However, for FLAIR ($80.0 pm7.1 , $), DWI ($78.2 pm7.9 , $) and CT ($79.1 pm7.9 , $), good-quality segmentation is feasible for most anatomicalstructures. Corrupted input volumes or low-quality segmentations can bedetected using dropout sampling. Conclusion: The flexibility and performance ofdeep convolutional neural networks enables the direct, real-time segmentationof FLAIR, DWI and CT scans without requiring $T 1$-weighted scans.

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