Key result
The proposed fully automatic deep-learning model achieved high accuracy for segmenting the aorta (94%) and lumen (96%) in CT images of patients with abdominal aortic aneurysms.
Why the study?
Accurate and automated techniques to segment the outer wall and lumen of abdominal aortic aneurysms were lacking, leading to simplified measurements or time-consuming manual segmentation with high operator variability.
Does a fully automatic deep learning-based model accurately segment abdominal aortic aneurysm tissues from CT imaging compared to expert manual segmentation?
Does a fully automatic deep learning-based model accurately segment abdominal aortic aneurysm tissues from CT imaging compared to expert manual segmentation?
A fully automatic deep learning model can accurately and efficiently segment abdominal aortic aneurysm tissues from CT images, demonstrating high agreement with expert manual segmentation.
May streamline AAA CT analysis; hypothesis-generating pending prospective clinical validation.
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.
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Abdolmanafi et al. (2023) studied Abdominal aortic aneurysm (n=81). Deep-learning based fully automatic segmentation model vs. Manual segmentation by an expert operator was evaluated on Segmentation accuracy for the aorta and lumen. The proposed fully automatic deep-learning model achieved high accuracy for segmenting the aorta (94%) and lumen (96%) in CT images of patients with abdominal aortic aneurysms.
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