LAUSR

PURPOSE This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques. METHODS Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio. Three convolutional neural network (CNN) models were developed on the training set (n = 246): single one-task (CNN1), single multi-task (CNN2), and serial multi-task (CNN3) models. Performance was evaluated using the Dice coefficient score (DCS) and lumen volume accuracy on the testing set (n = 30). Pearson correlation, Intra-class correlation coefficients and Bland-Altman plots were used to evaluate the inter-observer measurement agreement. RESULTS CNN3 performed the best, with mean DCSs of 0.93 ± 0.01, 0.93 ± 0.01 and 0.91 ± 0.02 for the whole aorta, TL, and FL, respectively (p < 0.05). Each label volume from CNN3 showed excellent agreement with the ground truth, with mean volume differences of -31.05 (-82.76 to 20.65) ml, 4.79 (-11.04 to 20.63) ml, and 8.67(-11.40 to 28.74) ml for the whole aorta, TL, and FL, respectively. The segmentation speed of CNN3 was 0.038 ± 0.006 s/image. CONCLUSION Deep learning-based model provides a promising approach for accurate and efficient segmentation of TBAD and makes it possible for automated measurements of TBAD anatomical features.