Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

• DOI: 10.3390/bioengineering9110648
• Publication Date: 2022-11-04T07:28:10Z
• Authors: Juhwan Lee; Justin N. Kim; Lia Gomez-Perez; Yazan Gharaibeh; Issam Motairek; Gabriel T. R. Per...; Vladislav N. Zimin; Luis A. P. Dallan; Ammar Hoori; Sadeer Al-Kindi; Giulio Guagliumi; Hiram G. Bezerra; David L. Wilson
• Keywords: FOS: Computer and information sciences; 0301 basic medicine; Technology; Computer Science - Machine Learning; optical coherence tomography; QH301-705.5; T; Computer Vision and Pattern Recognition (cs.CV); segmentation; Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; deep learning; Electrical Engineering and Systems Science - Image and Video Processing; Article; Machine Learning (cs.LG); 03 medical and health sciences; 0302 clinical medicine; classification; optical coherence tomography; microvessel; deep learning; segmentation; classification; microvessel; FOS: Electrical engineering, electronic engineering, information engineering; Biology (General)

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.