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

Xiaojun Yu; Mingshuai Li; Chenkun Ge; Miao Yuan; Linbo Liu; Jianhua Mo; Perry Ping Shum; Jinna Chen

doi:10.1016/j.compbiomed.2023.107319

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

Comput Biol Med. 2023 Oct:165:107319. doi: 10.1016/j.compbiomed.2023.107319. Epub 2023 Aug 12.

Authors

Xiaojun Yu¹, Mingshuai Li², Chenkun Ge³, Miao Yuan⁴, Linbo Liu⁵, Jianhua Mo⁶, Perry Ping Shum⁷, Jinna Chen⁸

Affiliations

¹ School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, China. Electronic address: XJYU@nwpu.edu.cn.
² School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China. Electronic address: Mingshuai_Li@mail.nwpu.edu.cn.
³ School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China. Electronic address: 2020262494@mail.nwpu.edu.cn.
⁴ School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China. Electronic address: yuanmiao@mail.nwpu.edu.cn.
⁵ School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. Electronic address: LIULINBO@ntu.edu.sg.
⁶ School of Electronics and Information Engineering, Soochow University, Suzhou 215006, China. Electronic address: jhmo@suda.edu.cn.
⁷ Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. Electronic address: shenp@sustech.edu.cn.
⁸ Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. Electronic address: chenjn@sustech.edu.cn.

PMID: 37611427
DOI: 10.1016/j.compbiomed.2023.107319

Abstract

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.

Keywords: Macular edema segmentation; Optical coherence tomography; Parallel decoding network.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Macular Edema* / diagnostic imaging
Retina / diagnostic imaging
Retinal Detachment* / diagnostic imaging
Tomography, Optical Coherence / methods