Brain Extraction in Susceptibility-Weighted MR Images using Deep Learning
K. Koschmieder, A. van der Eerden, B. van Ginneken and R. Manniesing
in: Annual Meeting of the Radiological Society of North America, 2018
PURPOSE: Brain extraction methods in MRI have so far been exclusively developed for T1- and T2-weighted images. A deep neural network is presented to segment the brain tissue in susceptibility-weighted images (SWI) in healthy individuals and patients with traumatic brain injury (TBI). MATERIALS AND METHODS: In total, MRI scans from 33 patients with moderate to severe TBI and 18 healthy controls were collected. SWIs were acquired with 27ms TR, 20ms TE, 15° flip angle, and0.98x0.98x1.00mm³ voxel size on a 3T Siemens MRI scanner. A small scale 2D-U-Net was implemented (18 convolution layers, max. 256 features per layer) processing a volume in axial direction. The U-Net architecture allowed the model to utilize both local and contextual information. The output probability maps were thresholded and possible outliers were removed by taking the largest connected component. 20 TBI patients and 10 controls served as a test set, the remaining patients were used for training. The reference standard were brain masks obtained with SPM, a publicly available software package commonly used for brain extractions in MR neuroimaging, but not optimized for SWI sequences. These annotations were visually inspected. The results of the deep learning method were visually inspected for completeness and overall quality. Dice similarity coefficient (DCS) and the modified Hausdorff (MHD) distance were reported for the test set. RESULTS: The DCS was 0.98±0.002 per volume at the chosen operating point on the SPM standard and the MHD was 0.93±0.11mm per volume. It took less than 10 seconds to compute the complete 3D brain mask on a modern GPU. Overall, our method was capable of learning from a sub-optimal reference standard and extracting the brain in an SWI image. It mimicked some of the deficiencies of the SPM brain masks, such as occasional failures in the most inferior or superior axial slices, but also mitigated others through generalization over the training set. Holes in the mask caused by contusions or hematomas were less prevalent with the 2D-U-Net than with SPM. CONCLUSION: The 2D-U-Net method provides fast brain extractions in MR-SWI.
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