  Type B Aortic Dissection(TBAD) is a rare aortic disease with a high 5-year
mortality.Personalized and precise management of TBAD has been increasingly
desired in clinic which requires the geometric parameters of TBAD specific to
the patient be measured accurately.This remains to be a challenging task for
vascular surgeons as manual measurement is highly subjective and imprecise. To
solve this problem,we introduce STENT-a STandard cta database with annotation
of the ENtire aorta and True-false lumen. The database contains 274 CT
angiography (CTA) scans from 274 unique TBAD patients and is split into a
training set(254 cases including 210 preoperative and 44 postoperative scans )
and a test set(20 cases).Based on STENT,we develop a series of methods
including automated TBAD segmentation and automated measurement of TBAD
parameters that facilitate personalized and precise management of the disease.
In this work, the database and the proposed methods are thoroughly introduced
and evaluated and the results of our study shows the feasibility and
effectiveness of our approach to easing the decision-making process for
vascular surgeons during personalized TBAD management.
