Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Vascular Biology

Background:

• Microvessels within vascular plaque are indicators of plaque progression, rupture, and intra-plaque hemorrhage.
• Analyzing these microvessels is crucial for understanding plaque vulnerability.

Purpose of the Study:

• To develop and validate an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images.
• To improve the efficiency and accuracy of microvessel detection in IVOCT for research and clinical applications.

Main Methods:

• Developed an automated deep learning system using DeepLab v3+ for microvessel segmentation and a shallow convolutional neural network for classification.
• Employed data augmentation in the polar domain and on bounding boxes, along with pre-processing steps like guidewire/shadow detection and noise reduction.
• Analyzed 8403 IVOCT image frames from 85 lesions and 37 normal segments, comparing automated results with manual annotations.

Main Results:

• The automated method achieved a Dice coefficient of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1% for segmentation.
• The classification network demonstrated high performance with 99.5 ± 0.3% sensitivity, 98.8 ± 1.0% specificity, and 99.1 ± 0.5% accuracy.
• The automated approach showed a 4.4% difference compared to manual analysis, with improved microvessel continuity.

Conclusions:

• The developed automated deep learning method effectively detects microvessels in IVOCT images, offering a valuable tool for plaque vulnerability assessment.
• This method shows promise for enhancing research into vascular plaque progression and informing future treatment planning.