Article
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Modeling Blood Vessels Lumen from 3D Images with the Use of Deep Learning and B-Splines
Version 1
: Received: 25 December 2023 / Approved: 26 December 2023 / Online: 26 December 2023 (06:14:29 CET)
A peer-reviewed article of this Preprint also exists.
Materka, A.; Jurek, J. Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images. Sensors 2024, 24, 846. Materka, A.; Jurek, J. Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images. Sensors 2024, 24, 846.
Abstract
Accurate geometric modeling of blood vessels lumen given their 3D images is a basis for vessel quantification in support of cardiovascular diseases diagnosis, treatment and monitoring. Unlike other approaches, which assume circular or elliptical vessel cross-section, the proposed method employs parametric B-splines combined with the image formation system equations to accurately localize the highly curved lumen boundaries. No image segmentation is involved which might reduce the localization accuracy in effect of spatial discretization. It is shown that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. Two example applications: modeling the lower extremities artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA), are presented and discussed to demonstrate the method potential. Besides application to medical diagnosis, blood-flow simulation and vessel-phantom design, it can serve as a tool for automated annotation of image datasets required to train machine-learning algorithms.
Keywords
Blood vessels; Lumen quantification; Centerline; Deep learning; 3D images; B-Splines; NURBS; Tubular objects
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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