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Proceedings Paper

Convolutional neural network (CNN) based needle-tracking for OCT-guided cornea “Big Bubble” procedure (Conference Presentation)

Paper 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.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII, 112431L (9 March 2020); doi: 10.1117/12.2546547
Show Author Affiliations
Ruizhi Zuo, Johns Hopkins Univ. (United States)
Jin U. Kang, Johns Hopkins Univ. (United States)
Soohyun Lee, Johns Hopkins Univ. (United States)
Shoujing Guo, Johns Hopkins Univ. (United States)
Shuwen Wei, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 11243:
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII
Daniel L. Farkas; Attila Tarnok, Editor(s)

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