1. Automatic Segmentation Methods Based on Machine Learning for Intracoronary Optical Coherence Tomography Image

    Automatic Segmentation Methods Based on Machine Learning for Intracoronary Optical Coherence Tomography Image

    Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness < 65 µm, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are still performed manually by physicians ...

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