WRCANet: wavelet residual cross-attention network for meibomian gland segmentation.

Zhaohui Yang, Weifang Zhu, Sanhua Xu, Fei Shi, Dehui Xiang, Tao Peng, Yi Shao, Xinjian Chen

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Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Zhaohui YangMIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.ORCID https://orcid.org/0009-0008-9746-0148

Weifang ZhuMIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.ORCID https://orcid.org/0000-0001-9540-4101

Sanhua XuDepartment of Ophthalmology, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China.

Fei ShiMIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.ORCID https://orcid.org/0000-0002-8878-6655

Dehui XiangMIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.ORCID https://orcid.org/0000-0001-7873-9778

Tao PengSchool of Future Science and Engineering, Soochow University, Suzhou 215222, China.

Yi ShaoDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai 200080, China.

Xinjian ChenMIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To address challenges in automatic meibomian gland segmentation for meibomian gland dysfunction (MGD) diagnosis, such as size variation and blurred edges, this study proposes a wavelet residual cross-attention network (WRCANet). The network utilizes wavelet residual blocks to preserve boundary details and a cross-attention-based global feature aggregation module to handle varying gland sizes. Evaluated on both in-house and public datasets, WRCANet achieves Dice scores of 90.20% and 84.60% respectively, outperforming other state-of-the-art methods. By effectively enhancing gland boundary discrimination, the model shows significant potential for precise morphological analysis and auxiliary clinical diagnosis of MGD.

Identifiers

PMID42111007

DOI10.1364/BOE.577609

PMCPMC13155841

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.