Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation
Journal:
arXiv
Published Date:
Jan 18, 2025
Abstract
Accurate segmentation of the aorta and its associated arch branches is
crucial for diagnosing aortic diseases. While deep learning techniques have
significantly improved aorta segmentation, they remain challenging due to the
intricate multiscale structure and the complexity of the surrounding tissues.
This paper presents a novel approach for enhancing aorta segmentation using a
Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model.
Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the
former provides an initial aorta segmentation, and the latter enhances blood
vessel detection across varying scales by learning suitable LoG kernels,
enabling self-adaptive handling of different parts of the aorta vessels with
significant scale differences. We employ a Bayesian method to parameterize the
LoG stream and provide confidence intervals for the segmentation results,
ensuring robustness and reliability of the prediction for vascular medical
image analysts. Experimental results show that our model can accurately segment
main and supra-aortic vessels, yielding at least a 3% gain in the Dice
coefficient over state-of-the-art methods across multiple volumes drawn from
two aorta datasets, and can provide reliable confidence intervals for different
parts of the aorta. The code is available at https://github.com/adlsn/LoGBNet.
