@Article{cmc.2020.09940,
AUTHOR = {Mei Yang, Yuanjie Zheng, Weikuan Jia, Tongtong Che, Jinyu Cong},
TITLE = {Joint Deep Matching Model of OCT Retinal Layer Segmentation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1485--1498},
URL = {http://www.techscience.com/cmc/v63n3/38888},
ISSN = {1546-2226},
ABSTRACT = {Optical Coherence Tomography (OCT) is very important in medicine and 
provide useful diagnostic information. Measuring retinal layer thicknesses plays a vital 
role in pathophysiologic factors of many ocular conditions. Among the existing retinal 
layer segmentation approaches, learning or deep learning-based methods belong to the 
state-of-art. However, most of these techniques rely on manual-marked layers and the 
performances are limited due to the image quality. In order to overcome this limitation, 
we build a framework based on gray value curve matching, which uses depth learning to 
match the curve for semi-automatic segmentation of retinal layers from OCT. The depth 
convolution network learns the column correspondence in the OCT image unsupervised. 
The whole OCT image participates in the depth convolution neural network operation, 
compares the gray value of each column, and matches the gray value sequence of the 
transformation column and the next column. Using this algorithm, when a boundary point 
is manually specified, we can accurately segment the boundary between retinal layers. 
Our experimental results obtained from a 54-subjects database of both normal healthy 
eyes and affected eyes demonstrate the superior performances of our approach.},
DOI = {10.32604/cmc.2020.09940}
}