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Proceedings Paper

Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography
Author(s): Yihao Liu; Lianrui Zuo; Aaron Carass; Yufan He; Angeliki Filippatou; Sharon D. Solomon; Shiv Saidha; Peter A. Calabresi; Jerry L. Prince
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Paper 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.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130Y (10 March 2020); doi: 10.1117/12.2549967
Show Author Affiliations
Yihao Liu, The Johns Hopkins Univ. (United States)
Lianrui Zuo, The Johns Hopkins Univ. (United States)
Aaron Carass, The Johns Hopkins Univ. (United States)
Yufan He, the Johns Hopkins Univ. (United States)
Angeliki Filippatou, The Johns Hopkins Univ. School of Medicine (United States)
Sharon D. Solomon, The Johns Hopkins Univ. School of Medicine (United States)
Shiv Saidha, The Johns Hopkins Univ. School of Medicine (United States)
Peter A. Calabresi, The Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, The Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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