Fully Automatic Segmentation of Type B Aortic Dissection from CTA Images Enabled by Deep Learning

Publication date: Available online 17 October 2019Source: European Journal of RadiologyAuthor(s): Long Cao, Ruiqiong Shi, Yangyang Ge, Lei Xing, Panli Zuo, Yan Jia, Jie Liu, Yuan He, Xinhao Wang, Shaoliang Luan, Xiangfei Chai, Wei GuoGraphical abstractDeep learning-based algorithm provides an automated segmentation solution for type B aortic dissection (TBAD) on original CTAs. The serial multi-task based CNN achieved the best Dice coefficient scores (0.93 ± 0.01, 0.93 ± 0.01, and 0.91 ± 0.02 for the whole aorta, true lumen, and false lumen, respectively) and obtained an aortic lumen volume close to that of the ground truth, with an acceptable segmentation speed of 0.038 ± 0.006 s per slice. These findings indicate that the proposed method promises to enable clinicians to extract a large amount of morphological information accurately and rapidly in clinical settings, and greatly facilitate the TBAD anatomical features measurement process. TBAD = type B aortic dissection; CNN = convolutional neural network; CTA = computed tomography angiography.
Source: European Journal of Radiology - Category: Radiology Source Type: research