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5 March 2021 Automated adipose segmentation within cardiac optical coherence tomography images
Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Andrew Laine, Elsa Angelini, Christine Hendon
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Proceedings Volume 11621, Diagnostic and Therapeutic Applications of Light in Cardiology 2021; 1162107 (2021) https://doi.org/10.1117/12.2578814
Event: SPIE BiOS, 2021, Online Only
Abstract
In this project, we propose a deep learning based weakly supervised learning algorithm for cardiac adipose tissue segmentation using image-level labels. Based on ReLayNet, our proposed method can automatically segment the adipose tissue from normal myocardium tissue in pixel level. Compared with fully supervised learning methods, our model achieves competitive segmentation results on both accuracy and Dice coefficient within a database of OCT images of human cardiac tissue. Combined with the OCT image, the predicted adipose map could provide additional information for the guidance of cardiac radio frequency ablation.
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Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Andrew Laine, Elsa Angelini, and Christine Hendon "Automated adipose segmentation within cardiac optical coherence tomography images", Proc. SPIE 11621, Diagnostic and Therapeutic Applications of Light in Cardiology 2021, 1162107 (5 March 2021); https://doi.org/10.1117/12.2578814
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KEYWORDS
Image segmentation
Optical coherence tomography
Heart
Tissues
Binary data
Image processing algorithms and systems
Image resolution
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