  Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular
events characterized by a growing yearly incidence,and the severity of disease
prognosis. Currently, computed tomography angiography (CTA) has been widely
adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true
lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial
for the precise quantification of anatomical features. However, existing works
only focus on only TL and FL without considering FLT. In this paper, we propose
ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of
TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD
CTA images, which is of decent size compared with existing medical imaging
datasets. As FLT can appear almost anywhere along the aorta with irregular
shapes, segmentation of FLT presents a wide class of segmentation problems
where targets exist in a variety of positions with irregular shapes. We further
propose a baseline method for automatic segmentation of TBAD. Results show that
the baseline method can achieve comparable results with existing works on aorta
and TL segmentation. However, the segmentation accuracy of FLT is only 52%,
which leaves large room for improvement and also shows the challenge of our
dataset. To facilitate further research on this challenging problem, our
dataset and codes are released to the public.
