Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography

Yihao Liu; Lianrui Zuo; Aaron Carass; Yufan He; Angeliki Filippatou; Sharon D. Solomon; Shiv Saidha; Peter A. Calabresi; Jerry L. Prince

doi:10.1117/12.2549967

10 March 2020 Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography

Yihao Liu, Lianrui Zuo, Aaron Carass, Yufan He, Angeliki Filippatou, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113130Y (2020) https://doi.org/10.1117/12.2549967
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Deep learning approaches have been used extensively for medical image segmentation tasks. Training deep networks for segmentation, however, typically requires manually delineated examples which provide a ground truth for optimization of the network. In this work, we present a neural network architecture that segments vascular structures in retinal OCTA images without the need of direct supervision. Instead, we propose a variational intensity cross channel encoder that finds vessel masks by exploiting the common underlying structure shared by two OCTA images of the the same region but acquired on different devices. Experimental results demonstrate significant improvement over three existing methods that are commonly used.

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Yihao Liu, Lianrui Zuo, Aaron Carass, Yufan He, Angeliki Filippatou, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, and Jerry L. Prince "Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130Y (10 March 2020); https://doi.org/10.1117/12.2549967

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