Translator Disclaimer
Paper
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 +
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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
7 PAGES


SHARE
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
Back to Top