文献类型：期刊文献

英文题名：A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation

作者：Chen, Qiong[1,2];Zeng, Lirong[3];Lin, Cong[1,3]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Haida Rd, Zhanjiang 524000, Guangdong, Peoples R China;[2]Tsinghua Univ, Dept Earth Syst Sci, Shuangqing Rd, Beijing 100084, Peoples R China;[3]Hainan Univ, Sch Informat & Commun Engn, Renmin Ave, Haikou 570228, Hainan, Peoples R China

年份：2023

卷号：13

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：SCI-EXPANDED(收录号：WOS:001031438900040)、、Scopus(收录号：2-s2.0-85145745218)、WOS

基金：This work was supported in part by the China Postdoctoral Science Foundation under Grant 2021M701838, in part by the National Natural Science Foundation of China under Grant 62272109, in part by the Guangdong University Student Science and Technology Innovation Cultivation Special Fund Support Project under Grant pdjh2023a0243, in part by the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2021019, and in part by the Zhanjiang Non-funded Science and Technology Research Program under Grant 2022B01079.

语种：英文

外文摘要：The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for OCT fundus image segmentation. Firstly, we establish the information decision table of OCT fundus image segmentation, and regard each category of segmentation region as a fuzzy set. Then, we use the fuzzy c-means clustering to get the membership degrees of pixels to each segmentation region. According to membership functions and the equivalence relation generated by the brightness attribute, we design the individual fitness function based on the rough fuzzy set, and use a genetic algorithm to search for the best breakpoints to discretize the features of OCT fundus images. Finally, we take the feature discretization based on the rough fuzzy set as the pre-module of the deep neural network, and introduce the deep supervised attention mechanism to obtain the important multi-scale information. We compare RFDDN with U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet on the two groups of 3D retinal OCT data. RFDDN is superior to the other five methods on all evaluation indicators. The results obtained by ISCLNet are the second only inferior to those obtained by RFDDN. DSC, sensitivity, and specificity of RFDDN are evenly 3.3%, 2.6%, and 7.1% higher than those of ISCLNet, respectively. HD95 and ASD of RFDDN are evenly 6.6% and 19.7% lower than those of ISCLNet, respectively. The experimental results show that our method can effectively eliminate the noise and redundant information in Oct fundus images, and greatly improve the accuracy of OCT fundus image segmentation while taking into account the interpretability and computational efficiency.