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HIERARCHICAL LOG BAYESIAN NEURAL NETWORK FOR ENHANCED AORTA SEGMENTATION.

Delin An¹, Pan Du², Pengfei Gu³

¹Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN.

Proceedings. IEEE International Symposium on Biomedical Imaging

|September 26, 2025

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This study introduces a novel Bayesian neural network model to improve aorta segmentation for disease diagnosis. The method enhances accuracy and provides reliable confidence intervals for medical image analysis.

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Science

Background:

Accurate aorta segmentation is vital for diagnosing aortic diseases.
Deep learning methods face challenges with the aorta's complex structure and surrounding tissues.

Purpose of the Study:

To enhance aorta segmentation accuracy and reliability using a novel deep learning approach.
To provide confidence intervals for segmentation results to aid medical image analysts.

Main Methods:

Developed a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model.
Integrated a 3D U-Net for initial segmentation and a hierarchical LoG stream for scale-adaptive vessel detection.
Employed Bayesian methods for LoG stream parameterization and confidence interval generation.

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Main Results:

Achieved accurate segmentation of the aorta and supra-aortic vessels.
Demonstrated at least a 3% gain in Dice coefficient over state-of-the-art methods on two aorta datasets.
Provided reliable confidence intervals for different aortic regions.

Conclusions:

The proposed Bayesian LoG model significantly improves aorta segmentation accuracy and robustness.
The model's ability to provide confidence intervals enhances its clinical utility for vascular medical image analysts.