Perceptual Data Augmentation for Biomedical Coronary Vessel Segmentation.

Area of Science:

• Medical Imaging
• Deep Learning
• Computer Vision

Background:

• Deep learning requires extensive annotated data, which is challenging for complex medical images like coronary angiograms.
• Unsupervised domain adaptation methods struggle with medical image characteristics, limiting their effectiveness.
• Existing data augmentation techniques offer limited improvements for specialized medical image segmentation.

Purpose of the Study:

• To propose an effective perceptual data augmentation method for vessel segmentation in coronary angiograms.
• To enhance the similarity between source (eye fundus) and target (coronary angiogram) domains in unsupervised domain adaptation.
• To address the difficulty of annotating small and complex vessel structures in medical imaging.

Main Methods:

• Developed a perceptual data augmentation method using synthesized virtual samples.
• Introduced the Auto Foreground Augment method to find geometric transformations for improved vessel similarity.
• Employed the Haar Wavelet-Based Perceptual Similarity Index to guide foreground and background mixup for sample synthesis.

Main Results:

• The proposed method successfully synthesizes high-quality virtual samples.
• Demonstrated significant improvement in domain adaptation performance for vessel segmentation.
• Achieved superior results compared to existing data augmentation methods in this specific task.

Conclusions:

• Perceptual data augmentation is effective for improving unsupervised domain adaptation in medical image segmentation.
• The novel method enhances the similarity between different medical imaging domains, like eye fundus and coronary angiograms.
• This work represents the first application of perceptual data augmentation to vessel segmentation in coronary angiograms.