@inproceedings{c95c9addedbc485b82aa66ea8f3aa667,
title = "Segmenting retinal OCT images with inter-B-scan and longitudinal information",
abstract = "Monitoring retinal thickness of persons with multiple sclerosis (MS) provides important bio-markers for disease progression. However, changes in retinal thickness can be small and concealed by noise in the acquired data. Consistent longitudinal retinal layer segmentation methods for optical coherence tomography (OCT) images are crucial for identifying the real longitudinal retinal changes of individuals with MS. In this paper, we propose an iterative registration and deep learning based segmentation method for longitudinal 3D OCT scans. Since 3D OCT scans are usually anisotropic with large slice separation, we extract B-scan features using 2D deep networks and utilize inter-B-scan context with convolutional long-short-term memory (LSTM). To incorporate longitudinal information, we perform fundus registration and interpolate the smooth retinal surfaces of the previous visit to use as a prior on the current visit.",
keywords = "Conv-LSTM, Fully convolutional network, Longitudinal, OCT, Retina",
author = "Yufan He and Aaron Carass and Yihao Liu and Angeliki Filippatou and Jedynak, {Bruno M.} 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 under grant R01-EY024655 and R01NS082347. 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 = "10.1117/12.2549857",
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",
}