Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images.

ABSTRACT: Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate 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. We present an

SUBMITTER: Materka A 

PROVIDER: S-EPMC10857344 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature