Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model

• Published in: Computers in biology and medicine Vol. 179; p. 108838
• Main Authors: Lyu, Zonghan, Mu, Nan, Rezaeitaleshmahalleh, Mostafa, Zhang, Xiaoming, McBane, Robert, Jiang, Jingfeng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2024; Elsevier Limited
• Subjects: Abdomen; Abdominal aortic aneurysm; Aneurysms; Angiography; Aorta; Aortic Aneurysm, Abdominal - diagnostic imaging; Aortic aneurysms; Automation; Blood clots; Blood vessels; Computed tomography; Computed Tomography Angiography - methods; Contrast agents; Contrast media; Coronary vessels; Deep learning; Deep-learning neural network; Delineation; Humans; Image enhancement; Image segmentation; Internal Medicine; Intraluminal thrombosis; Learning; Machine learning; Magnetic resonance imaging; Male; Medical imaging; Morphology; Multiview perception; Neural networks; Neural Networks, Computer; Optimization techniques; Other; Perception; Radiomics; Reagents; Segmentation; Thrombosis; Thrombosis - diagnostic imaging; Ultrasonic imaging; Workflow; Image segmentation; Intraluminal thrombosis; Multiview perception; Deep-learning neural network; Abdominal aortic aneurysm
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108838

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow. [Display omitted] •This paper proposes a mixed-scale multiview pipeline for accurate ILT segmentation.•Mixed-scale 2D CNN is used to extract information from three orthogonal views.•We leveraged multiview information to enrich prior knowledge for 3D ILT learning.•We designed a context-aware CNN to fully integrate multiview ILT-related objects.