Exploratory analysis of Type B Aortic Dissection (TBAD) segmentation in 2D CTA images using various kernels.

Autor: Abaid A; School of Computer Science, University of Galway, Galway, Ireland., Ilancheran S; School of Computer Science, University of Galway, Galway, Ireland., Iqbal T; Insight SFI Research Centre for Data Analytics, University of Galway, Galway, Ireland., Hynes N; University Hospital Galway, Newcastle Road, University of Galway, Galway, Ireland., Ullah I; Insight SFI Research Centre for Data Analytics, University of Galway, Galway, Ireland; School of Computer Science, University of Galway, Galway, Ireland. Electronic address: ihsan.ullah@universityofgalway.ie.
Jazyk: angličtina
Zdroj: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2024 Nov 18; Vol. 118, pp. 102460. Date of Electronic Publication: 2024 Nov 18.
DOI: 10.1016/j.compmedimag.2024.102460
Abstrakt: Type-B Aortic Dissection is a rare but fatal cardiovascular disease characterized by a tear in the inner layer of the aorta, affecting 3.5 per 100,000 individuals annually. In this work, we explore the feasibility of leveraging two-dimensional Convolutional Neural Network (CNN) models to perform accurate slice-by-slice segmentation of true lumen, false lumen and false lumen thrombus in Computed Tomography Angiography images. The study performed an exploratory analysis of three 2D U-Net models: the baseline 2D U-Net, a variant of U-Net with atrous convolutions, and a U-Net with a custom layer featuring a position-oriented, partially shared weighting scheme kernel. These models were trained and benchmarked against a state-of-the-art baseline 3D U-Net model. Overall, our U-Net with the VGG19 encoder architecture achieved the best performance score among all other models, with a mean Dice score of 80.48% and an IoU score of 72.93%. The segmentation results were also compared with the Segment Anything Model (SAM) and the UniverSeg models. Our findings indicate that our 2D U-Net models excel in false lumen and true lumen segmentation accuracy while achieving lower false lumen thrombus segmentation accuracy compared to the state-of-the-art 3D U-Net model. The study findings highlight the complexities involved in developing segmentation models, especially for cardiovascular medical images, and emphasize the importance of developing lightweight models for real-time decision-making to improve overall patient care.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Ltd.)
Databáze: MEDLINE
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