Fast, Robust and Accurate Segmentation of the Complete Cerebral Vasculature in 4D-CTA using Deep Learning

M. Meijs, A. Patel, S.C. van de Leemput, B. van Ginneken, M. Prokop and R. Manniesing

in: Annual Meeting of the Radiological Society of North America, 2018

Abstract

PURPOSE: Segmentation of the complete cerebral vasculature in 4D-CTA is important for improved visualization, automated pathology detection and assessment of the collateral flow. We present a deep learning approach to segment the complete cerebral vasculature in 4D-CTA of patients with suspected stroke. MATERIALS AND METHODS: In total 162 patients that underwent 4D-CTA for suspicion of stroke were included in this study. The scans were acquired on a 320-detector row scanner (Canon Medical Systems Corporation, Japan). Image size was 512x512x320 voxels by 19 time points with isotropic voxel sizes of approximately 0.5 mm. A 3D fully convolutional neural network (CNN), U-Net, was proposed with integration of a spatial feature in the final convolutional layer of the network. The weighted temporal average and variance were derived from the 4D-CTA and used as input for the network. As spatial feature the Euclidean distance from the center of the brain to the skull was used. Training was done on 19 patients with manually annotated data. The remaining 143 patients were used as testing set. Segmentations were visually inspected for completeness and overall quality. Two observers manually annotated three dimensional sub-volumes throughout the brain to include different sized vessels for quantitative evaluation. The Dice similarity coefficient (DSC) and Mean Contour Distance (MCD) of the segmentations were reported. RESULTS Overall the method was capable of segmenting the complete cerebral vasculature. Smaller distal vessels (e.g. M3) showed similar segmentation results as the larger vessels (e.g. internal carotid artery). The DSC was 0.91±0.08 and the MCD was 0.26±0.24 mm which is below voxel spacing. Computation time was less than 90 seconds for processing a full 4D-CTA data set. CONCLUSION: A 3D U-Net with spatial features provides fast, robust and accurate segmentations of the full cerebral vasculature in 4D-CTA. Clinical Relevance The high quality segmentation provided by our method is an important step towards the automated localization and evaluation of vascular pathology in acute stroke patients.

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