Lumen boundary detection using neutrosophic c-means in IVOCT images

Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsules

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. Current image processing methods are hindered by the time needed to generate expert-labelled datasets and the potential for bias during the analysis. Therefore, automated, robust, unbiased and timely geometry extraction from IVOCT, using image processing, would be beneficial to clinicians. With clinical application in mind, we aim to develop a model with a small memory footprint that is fast at inference time witho…

OCTOPUS -- optical coherence tomography plaque and stent analysis software

Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks. Interactive visualization and manual editing of segmentations were included in the s…

Automated segmentation of microvessels in intravascular OCT images using deep learning

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 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done 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 shift…

血管内光学相干断层扫描图像中纤维帽的深度学习分割

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images

本研究开发了一种全自动深度学习分割IVOCT图像中纤维帽的方法，采用改进的SegResNet和迁移学习，在分割精度上优于其他方法，并具有高泛化性和临床可用性。