A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.

Area of Science:

• Medical Image Analysis
• Cardiovascular Imaging
• Deep Learning

Background:

• Accurate blood vessel segmentation is crucial for diagnosing cardiovascular diseases and planning treatments.
• Coronary computed tomography angiography (CTA) images present segmentation challenges due to noise and complex vessel structures.
• Standard deep learning models like U-Net show moderate accuracy (Dice score 0.722) on CTA images.

Purpose of the Study:

• To enhance blood vessel segmentation in coronary CTA images.
• To improve the accuracy and robustness of deep learning models in segmenting complex vascular structures.
• To address the limitations of existing methods in handling noise and intricate geometries in CTA data.

Main Methods:

• Introduction of Morpho-U-Net, an enhanced U-Net architecture.
• Integration of advanced morphological operations: Gaussian blurring, thresholding, and morphological opening/closing.
• Application of pre-processing filters to reduce noise and group similar intensity pixels.

Main Results:

• Morpho-U-Net achieved a significantly higher Dice score of 0.9108.
• Achieved precision of 0.9341 and recall of 0.8872.
• Demonstrated superior robustness to noise and complex vessel geometries compared to classical methods.

Conclusions:

• The Morpho-U-Net architecture effectively improves vascular integrity and reduces noise in CTA images.
• The integrated pre-processing filter enables the model to focus on relevant anatomical structures.
• This approach outperforms traditional methods for blood vessel segmentation in challenging CTA datasets.