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7 March 2022 Human colorectal cancer assessment using optical coherence tomography catheter system paired with ResNet
Shuying Li, Hongbo Luo, Yifeng Zeng, Hassam Cheema, Ebunoluwa Otegbeye, William C. Chapman Jr., Matthew Mutch, Chao Zhou, Quing Zhu
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Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC1194808 (2022) https://doi.org/10.1117/12.2607970
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
In this study, we propose to combine miniaturized optical coherence tomography (OCT) catheter with a residual neural network (ResNet)-based deep learning model for differentiation of normal from cancerous colorectal tissue in fresh ex vivo specimens. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~10 um, and an axial resolution of 6 um. A customized ResNet-based neural network structure was trained on both benchtop and catheter images. An AUC of 0.97 was achieved to distinguish between normal and cancerous colorectal tissue when testing on the rest of catheter images.
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Shuying Li, Hongbo Luo, Yifeng Zeng, Hassam Cheema, Ebunoluwa Otegbeye, William C. Chapman Jr., Matthew Mutch, Chao Zhou, and Quing Zhu "Human colorectal cancer assessment using optical coherence tomography catheter system paired with ResNet", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC1194808 (7 March 2022); https://doi.org/10.1117/12.2607970
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KEYWORDS
Optical coherence tomography
Colorectal cancer
Tissues
Endoscopy
Neural networks
Cameras
Diagnostics
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