Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network.

Yuanqiong Chen, Zhijie Liu, Yujia Meng, Jianfeng Li

Abstract read

In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Automated cup-to-disc ratio quantification via color fundus photography for chronic glaucoma screening.BMC medical imaging · 2025
Article
Unveiling the black box: Explainable transfer learning for ocular disorder diagnosis.Digital health
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuanqiong ChenSchool of Computer Science and Engineering, Central South University, Changsha 410000, China.

Zhijie LiuSchool of Computer Science and Engineering, Jishou University, Zhangjiajie 427000, China.

Yujia MengSchool of Computer Science and Engineering, Jishou University, Zhangjiajie 427000, China.

Jianfeng LiSchool of Computer Science and Engineering, Jishou University, Zhangjiajie 427000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.

Indexed as

adversarial generative networkconvolutional neural networkglaucoma screeningoptic disc and optic cup segmentation

Identifiers

PMID39451843

DOI10.3390/biomimetics9100637

PMCPMC11506706

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.