Automatic Segmentation in 3D CT Images: A Comparative Study of Deep Learning Architectures for the Automatic Segmentation of the Abdominal Aorta

Abdominal aortic aneurysm (AAA) is a complex vascular condition associated with high mortality rates. Accurate abdominal aorta segmentation is essential in medical imaging, facilitating diagnosis and treatment for a range of cardiovascular diseases. In this regard, deep learning-based automated segm...

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Vydané v:Electronics (Basel) Ročník 13; číslo 24; s. 4919
Hlavní autori: Mavridis, Christos, Vagenas, Theodoros P., Economopoulos, Theodore L., Vezakis, Ioannis, Petropoulou, Ourania, Kakkos, Ioannis, Matsopoulos, George K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2024
Predmet:
Abdomen
Accuracy
Aneurysms
Aorta
Care and treatment
Clinical medicine
Comparative analysis
Comparative studies
Computed tomography
Coronary vessels
CT imaging
Datasets
Deep learning
Health aspects
Hospitals
Image segmentation
Machine learning
Medical imaging
Medical imaging equipment
Mortality
Neural networks
Tomography
Visualization
ISSN:2079-9292, 2079-9292
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Shrnutí:Abdominal aortic aneurysm (AAA) is a complex vascular condition associated with high mortality rates. Accurate abdominal aorta segmentation is essential in medical imaging, facilitating diagnosis and treatment for a range of cardiovascular diseases. In this regard, deep learning-based automated segmentation has shown significant promise in the precise delineation of the aorta. However, comparisons across different models remain limited, with most studies performing algorithmic training and testing on the same dataset. Furthermore, due to the variability in AAA presentation, using healthy controls for deep learning AAA segmentation poses a significant challenge. This study provides a detailed comparative analysis of four deep learning architectures—UNet, SegResNet, UNet Transformers (UNETR), and Shifted-Windows UNet Transformers (SwinUNETR)—for full abdominal aorta segmentation. The models were evaluated both qualitatively and quantitatively using private and public 3D (Computed Tomography) CT datasets. Moreover, they were successful in attaining high performance in delineating AAA aorta, while being trained on healthy aortic imaging data. Our findings indicate that the UNet architecture achieved the highest segmentation accuracy among the models tested.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13244919