  Segmentation of the entire aorta and true-false lumen is crucial to inform
plan and follow-up for endovascular repair of the rare yet life threatening
type B aortic dissection. Manual segmentation by slice is time-consuming and
requires expertise, while current computer-aided methods focus on the
segmentation of the entire aorta, are unable to concurrently segment true-false
lumen, and some require human interaction. We here report a fully automated
approach based on a 3-D multi-task deep convolutional neural network that
segments the entire aorta and true-false lumen from CTA images in a unified
framework. For training, we built a database containing 254 CTA images (210
preoperative and 44 postoperative) obtained using various systems from 254
unique patients with type B aortic dissection. Slice-wise manual segmentation
of the entire aorta and the true-false lumen for each 3-D CTA image was
provided. Upon evaluation of another 16 CTA images (11 preoperative and 5
postoperative) with ground truth segmentation provided by experienced vascular
surgeons, our method achieves a mean dice similarity score(DSC) of 0.910,0.849
and 0.821 for the entire aorta,true lumen and false lumen respectively.
