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Article Dans Une Revue Computer Methods and Programs in Biomedicine Année : 2021

Dissected aorta segmentation using convolutional neural networks

Résumé

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.
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Dates et versions

hal-03379667 , version 1 (18-10-2021)

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Tianling Lyu, Guanyu Yang, Xingran Zhao, Huazhong Shu, Limin Luo, et al.. Dissected aorta segmentation using convolutional neural networks. Computer Methods and Programs in Biomedicine, 2021, 211, pp.106417. ⟨10.1016/j.cmpb.2021.106417⟩. ⟨hal-03379667⟩
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