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Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
We developed a fully-automated deep learning method for FC segmentation.
Automated segmentation of microvessels in intravascular OCT images using deep learning
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%.
Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography
CONCLUSIONS We used calcifications and lumen features to identify lesions at risk of stent under-expansion.
OCTOPUS -- optical coherence tomography plaque and stent analysis software
Only up to 3. 8% of plaque pixels were modified, leading to an average editing time of only 7. 5 seconds/frame, an approximately 80% reduction compared to manual analysis.
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries
We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images.
