@inproceedings{935f14fa47d242abbec3e59f122d1056,
title = "Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography",
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.",
keywords = "Image synthesis, OCTA, Unsupervised segmentation, Variational autoencoder, Vessel segmentation",
author = "Yihao Liu and Lianrui Zuo and Aaron Carass and Yufan He and Angeliki Filippatou and Solomon, {Sharon D.} and Shiv Saidha and Calabresi, {Peter A.} and Prince, {Jerry L.}",
note = "Funding Information: This work was supported by the NIH/NEI grant R01-EY024655 and NIH/NINDS grant R01-NS082347. Publisher Copyright: {\textcopyright} 2020 SPIE. All rights reserved.; Medical Imaging 2020: Image Processing ; Conference date: 17-02-2020 Through 20-02-2020",
year = "2020",
doi = "10.1117/12.2549967",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2020",
}