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Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment

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Abstract

Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch’s Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.

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Data Availability

Two datasets used are publically available. The local one is currently not publically available.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62176190.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xiaoming Liu & Xiao Li

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, 430065, China

    Xiaoming Liu & Xiao Li

  3. Wuhan Aier Eye Hospital of Wuhan University, Wuhan, China

    Ying Zhang & Man Wang

  4. Department of Ophthalmology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China

    Junping Yao

  5. Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, 22030, USA

    Jinshan Tang

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  1. Xiaoming Liu
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Contributions

Xiaoming Liu, Jinshan Tang, and Xiao Li contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ying Zhang, Man Wang, and Xiao Li. The first draft of the manuscript was written by Xiao Li, Xiaoming Liu revised the manuscript, and all authors reviewed and edited previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xiaoming Liu.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was waived by the Ethics Committee with the retrospective and anonymous investigation.

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Liu, X., Li, X., Zhang, Y. et al. Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01093-y

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