A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection. (September 2020)

• Record Type: Journal Article
• Authors: Cheng, Junlong; Tian, Shengwei; Yu, Long; Ma, Xiang; Xing, Yan
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Deep learning algorithm -- Contrast-enhanced CT images -- Segmentation and detection of aortic dissection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102145
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper proposes a deep learning algorithm for rapid detection of aortic dissection using contrast-enhanced CT images. We built a segmentation network that is more suitable for segmenting CT images of the aorta. This research has laid the foundation for the development of an effective aortic dissection detection algorithm. Abstract: Aortic dissection (AD) is one of the most common aortic diseases, where blood enters the aortic wall through the aortic intimal rift and causes separation of the arterial wall. Any delay or misdiagnosis can have severe consequences for patients with aortic dissection and even lead to higher mortality rates. Therefore, rapid and accurate detection of aortic dissection saves patients valuable time and provides assistance for the selection of clinical treatment options. This paper describes a deep learning algorithm that uses contrast-enhanced CT images for segmentation and automatic detection of aortic dissection. First, we construct a U-Net based semantic segmentation architecture and apply it to contrast-enhanced CT images to segment the aortic true lumen. Then, we use the segmentation results for aortic circularity analysis to obtain slice-level detection results. Finally, we aggregated the slice-level results to present patient-level detection results. We tested our algorithm on 20 contrast-enhanced CT datasets, of which 10 were aortic dissections. In terms of temporal performance, we have achieved millisecond prediction on sliced images. At the same time, we achieved 85.00% accuracy, 90.00% sensitivity and 80.00% specificity in patient-level testing.