摘要
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.
基金
This work was supported in part by the National Nature Science Foundation of China under Grant 61572300,Grant 81871508,and Grant61773246
in part by the Taishan Scholar Program of Shandong Province of China under Grant TSHW201502038
in part by the Major Program of Shandong Province Natural Science Foundation under Grant ZR2018ZB0419
in part by the Primary Research and Development Plan of Shandong Province under Grant 2017GGX10112,2019GNC106115
Shandong Province Higher Educational Science and Technology Program(No.J18KA308).
