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9 March 2020 Convolutional neural network (CNN) based needle-tracking for OCT-guided cornea “Big Bubble” procedure (Conference Presentation)
Ruizhi Zuo, Jin U. Kang, Soohyun Lee, Shoujing Guo, Shuwen Wei
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Proceedings Volume 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII; 112431L (2020) https://doi.org/10.1117/12.2546547
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
In a partial cornea transplant surgery, a procedure known as “Big Bubble” is used and it requires precise needle detection and tracking. To accomplish this goal, we used traditional image segmentation methods and trained a Convolutional Neural network (CNN) model to track the needle during the cornea transplant surgery guided by OCT B-scan imaging. The dataset was generated from the laboratory OCT system and we classified them to three categories. The network architecture is based on U-Net and modified to avoid overfitting. We are able to track the needle and detect the distance between the needle tip and cornea bottom layer based on these results.
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Ruizhi Zuo, Jin U. Kang, Soohyun Lee, Shoujing Guo, and Shuwen Wei "Convolutional neural network (CNN) based needle-tracking for OCT-guided cornea “Big Bubble” procedure (Conference Presentation)", Proc. SPIE 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII, 112431L (9 March 2020); https://doi.org/10.1117/12.2546547
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KEYWORDS
Cornea
Convolutional neural networks
Optical coherence tomography
Surgery
Image segmentation
Hough transforms
Image filtering
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