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

Towards Personalized Management of Type B Aortic Dissection Using STENT:  a STandard cta database with annotation of the ENtire aorta and True-false  lumen