Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abstract

Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms

“…The region of interest for the analysis was defined as the abdominal aorta, specifically from below the celiac artery to the common iliac bifurcation, which were used as landmarks to ensure evaluation of the same portion of the artery at baseline and follow-up. The aortic wall and lumen for each patient were segmented semi-automatically 13 from both the baseline and follow-up CTAs, and the 3D geometry of both structures was extracted as a triangulated surface mesh by using the imaging software Simpleware ScanIP (Synopsys).…”

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

“…In summary, all steps of this classifier yielded the expected results. Another researcher, Abdolmanafi ( 22 ), utilized a Resnet-based fully convolutional network (FCN) with dilated convolutions as the deep learning architecture. They employed experienced vascular radiologists to manually delineate the contours of the aorta, the wall, and the intraluminal structures, and used the results of these expert delineations as the gold standard to train the deep learning model.…”

A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images

“…To demonstrate the superiority of our DCTN, we have compared it with the existing methods of aortic segmentation, including Xiong [21], Sieren [27], Li [24], Feiger [19], Wobben [23], Song [34], Hahn [28], Abdolmanafi [47], Chen [26], Lyu [48], Zhao [36] , Deng [32], Yu [49], Cheng [50], and Cao [22]. We visualize the segmentation results of DCTN and other top seven comparison methods in Fig.…”