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BIF-Net: Boundary information fusion network for abdominal aortic aneurysm segmentation
Mingyu Wan1, Jing Zhu1, Yue Che1
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
Computers in Biology and Medicine
|October 11, 2024
Summary
A new Boundary Information Fusion Network (BIF-Net) improves abdominal aortic aneurysm (AAA) segmentation accuracy. This method enhances boundary feature extraction, outperforming existing techniques for better clinical diagnosis and treatment planning.
Area of Science:
- Medical Imaging
- Computer Vision
- Biomedical Engineering
Background:
- Accurate abdominal aortic aneurysm (AAA) segmentation is crucial for clinical diagnosis and treatment planning.
- Existing methods struggle with AAA segmentation due to scale variability and poor utilization of vessel boundary information.
Purpose of the Study:
- To introduce a novel network, BIF-Net, specifically designed for improved AAA segmentation.
- To enhance the capture of diverse AAA shapes and boundary features.
Main Methods:
- Developed BIF-Net incorporating Gabor and Sobel operators for enriched semantic and localization features via the GSDC module.
- Integrated GFFS and CSAM modules to recover lost boundary information during sampling.
- Implemented a boundary feature loss function to address sample imbalance.
Main Results:
- BIF-Net achieved Dice similarity coefficient (DSC) accuracies of 93.29% (preoperative) and 91.01% (postoperative).
- Outperformed state-of-the-art methods, improving DSC by 6.86% and 3.85% on respective datasets.
- Demonstrated superior performance in extracting boundary features for AAA segmentation.
Conclusions:
- BIF-Net offers a significant advancement in AAA segmentation accuracy.
- The network's robust boundary feature extraction capabilities show strong potential for clinical applications in AAA diagnosis and treatment.
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