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9 March 2020 Deep learning segmentation used in IVOCT images to guide optical attenuation imaging for plaque characterization (Conference Presentation)
Shengnan Liu, Denis Shamonin, Guillaume Zahnd, Joost Daemen, A. F. W. van der Steen, Theo van Walsum, Gijs van Soest
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Proceedings Volume 11215, Diagnostic and Therapeutic Applications of Light in Cardiology 2020; 1121506 (2020) https://doi.org/10.1117/12.2545639
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
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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Shengnan Liu, Denis Shamonin, Guillaume Zahnd, Joost Daemen, A. F. W. van der Steen, Theo van Walsum, and Gijs van Soest "Deep learning segmentation used in IVOCT images to guide optical attenuation imaging for plaque characterization (Conference Presentation)", Proc. SPIE 11215, Diagnostic and Therapeutic Applications of Light in Cardiology 2020, 1121506 (9 March 2020); https://doi.org/10.1117/12.2545639
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KEYWORDS
Signal attenuation
Image segmentation
Optical imaging
3D modeling
3D vision
3D visualizations
Arteries
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