Light attenuation has been used for a better understanding of plaque build-up in coronary arteries. The current analysis is only useful in diseased segments. We applied an automated detection using a deep-learning approach to identify the diseased areas. A U-net was trained to detect the lumen, the guide-wire structure, healthy vessel wall, and the diseased vessel wall. The trained network achieves an average Dice index of 0.88±0.02. Applying it to all images of the testing pullbacks, diseased areas were segmented. The attenuation was estimated in this area and can be visualized in a 3-D view reconstructed using the detected lumen regions.
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