Loss-balanced parallel decoding network for retinal fluid segmentation in OCT

Yu，Xiaojun1,2; Li，Mingshuai1; Ge，Chenkun1; Yuan，Miao1; Liu，Linbo3; Mo，Jianhua4; Shum，Perry Ping5; Chen，Jinna5

2023-10-01

10.1016/j.compbiomed.2023.107319

Computers in Biology and Medicine   Impact Factor & Quartile

0010-4825

1879-0534

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.

Macular edema segmentation Optical coherence tomography Parallel decoding network

SCI

English

Corresponding

Basic and Applied Basic Research Foundation of Guangdong Province[2021B1515120013];National Natural Science Foundation of China[62220106006];

Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology

Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology

WOS:001061367100001

PERGAMON-ELSEVIER SCIENCE LTD

COMPUTER SCIENCE

2-s2.0-85168423531

Scopus

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

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).