Dissected aorta segmentation using convolutional neural networks. (November 2021)

• Record Type: Journal Article
• Authors: Lyu, Tianling; Yang, Guanyu; Zhao, Xingran; Shu, Huazhong; Luo, Limin; Chen, Duanduan; Xiong, Jiang; Yang, Jian; Li, Shuo; Coatrieux, Jean-Louis; Chen, Yang
• Is Part Of: Computer methods and programs in biomedicine. Volume 211(2021)
• Publication Date: 2021-11
• Subjects: Aorta dissection -- Computed tomography -- Deep learning -- Image segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106417
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An algorithm for dissected aorta segmentation based on 3-D and 2-D convolutional neural networks. A 3-D CNN model identifies the proximal and distal regions of the dissected aorta data. A 2-D CNN model for aorta segmentation which incorporates boundary information. Abstract: Background and objective: Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed. Method: In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area. Results: The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%. Conclusions: The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps.