  Deep learning approaches may help radiologists in the early diagnosis and
timely treatment of cerebrovascular diseases. Accurate cerebral vessel
segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) is an
essential step in this process. This study investigates deep learning
approaches for automatic, fast and accurate cerebrovascular segmentation for
TOF-MRAs. The performance of several data augmentation and selection methods
for training a 2D and 3D U-Net for vessel segmentation was investigated in five
experiments: a) without augmentation, b) Gaussian blur, c) rotation and
flipping, d) Gaussian blur, rotation and flipping and e) different input patch
sizes. All experiments were performed by patch-training both a 2D and 3D U-Net
and predicted on a test set of MRAs. Ground truth was manually defined using an
interactive threshold and region growing method. The performance was evaluated
using the Dice Similarity Coefficient (DSC), Modified Hausdorff Distance and
Volumetric Similarity, between the predicted images and the interactively
defined ground truth. The segmentation performance of all trained networks on
the test set was found to be good, with DSC scores ranging from 0.72 to 0.83.
Both the 2D and 3D U-Net had the best segmentation performance with Gaussian
blur, rotation and flipping compared to other experiments without augmentation
or only one of those augmentation techniques. Additionally, training on larger
patches or slices gave optimal segmentation results. In conclusion, vessel
segmentation can be optimally performed on TOF-MRAs using a trained 3D U-Net on
larger patches, where data augmentation including Gaussian blur, rotation and
flipping was performed on the training data.
