Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.
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...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3101 - 3131 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283953&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283953 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283953 182283953 182283953 10.1007/s10278-024-01093-y 182283953 ppf: 3101 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment. aug: au: Liu, Xiaoming Li, Xiao Zhang, Ying Wang, Man Yao, Junping Tang, Jinshan affil: https://ror.org/00e4hrk88 School of Computer Science and Technology, Wuhan University of Science and Technology, 430065, Wuhan, China sug: subj: Retina Anatomy and Histology Retinal Detachment Diagnosis Tomography, Optical Coherence Human Funding Source Machine Learning Algorithms ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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