Vessel Contour Detection in Intracoronary Images via Bilateral Cross-Domain Adaptation

Area of Science:

• Medical Imaging
• Computer Vision
• Biomedical Engineering

Background:

• Vessel contour detection (VCD) is crucial for quantitative assessment in intravascular imaging but is challenged by vessel morphology variability.
• Single-modality imaging limits morphological information extraction, hindering VCD accuracy.
• Cross-modality methods offer potential but struggle with domain discrepancies (feature and label spaces).

Purpose of the Study:

• To address domain discrepancy challenges in cross-modality vessel contour detection (VCD).
• To improve VCD accuracy by integrating information from multiple intravascular imaging modalities.

Main Methods:

• Developed a novel method to divide label spaces into private and shared components, minimizing task risk at subdomain levels.
• Employed domain adaptation to extract domain-invariant features, overcoming feature space discrepancy.
• Utilized extracted domain-invariant features as auxiliary information for each subdomain.

Main Results:

• Achieved high effectiveness in VCD, demonstrated by a Dice index of 0.949.
• Outperformed nineteen state-of-the-art VCD methods in extensive experiments.
• Validated on 130 IVUS sequences (135,663 images) and 124 OCT sequences (39,857 images).

Conclusions:

• The proposed cross-modality method effectively overcomes domain discrepancy for VCD.
• This approach enhances VCD accuracy and robustness compared to existing single-modality and cross-modality techniques.
• The method shows significant potential for improving quantitative assessment in intravascular imaging.