Segmentation of the arteries and veins of the cerebral vasculature is important for improved visualization and for the detection of vascular related pathologies including arteriovenous malformations. We propose a 3D fully convolutional neural network (CNN) using a time-to-signal image as input and the distance to the center of gravity of the brain as spatial feature integrated in the final layers of the CNN. The method was trained and validated on 6 and tested on 4 4D CT patient imaging data. The reference standard was acquired by manual annotations by an experienced observer. Quantitative evaluation showed a mean Dice similarity coefficient of
0.94 ± 0.03 and 0.97 ± 0.01, a mean absolute volume difference of 4.36 ± 5.47 % and 1.79 ± 2.26 % for artery and vein respectively and an overall accuracy of 0.96 ± 0.02. The average calculation time per volume on the test set was approximately one minute. Our method shows promising results and enables fast and accurate segmentation of arteries and veins in full 4D CT imaging data.
Midas Meijs and Rashindra Manniesing, "Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751Q (Presented at SPIE Medical Imaging: February 15, 2018; Published: 27 February 2018); https://doi.org/10.1117/12.2292974.
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