Loss-balanced parallel decoding network for retinal fluid segmentation in OCT
As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.
Basic and Applied Basic Research Foundation of Guangdong Province[2021B1515120013];National Natural Science Foundation of China;
|WOS Research Area|
Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Electrical and Electronic Engineering|
1.School of Automation,Northwestern Polytechnical University,Xi'an,Shaanxi,710072,China
2.Shenzhen Research Institute of Northwestern Polytechnical University,Shenzhen,Guangdong,518057,China
3.School of Electrical and Electronic Engineering,Nanyang Technological University,639798,Singapore
4.School of Electronics and Information Engineering,Soochow University,Suzhou,215006,China
5.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
|Corresponding Author Affilication||Department of Electrical and Electronic Engineering|
Yu，Xiaojun,Li，Mingshuai,Ge，Chenkun,et al. Loss-balanced parallel decoding network for retinal fluid segmentation in OCT[J]. Computers in Biology and Medicine,2023,165.
Yu，Xiaojun.,Li，Mingshuai.,Ge，Chenkun.,Yuan，Miao.,Liu，Linbo.,...&Chen，Jinna.(2023).Loss-balanced parallel decoding network for retinal fluid segmentation in OCT.Computers in Biology and Medicine,165.
Yu，Xiaojun,et al."Loss-balanced parallel decoding network for retinal fluid segmentation in OCT".Computers in Biology and Medicine 165(2023).
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