Evidence map›Paper›PMID 42177481›Full record

ArticleBMC ophthalmology2026

Automated measurement of the minimum macular hole diameter based on optical coherence tomography images.

Jianguo Xu, Jiahao Wei, Rong Tan, Jianxin Shen, Jin Yao, Fen Zhou

Abstract read
In one paragraph

Article in BMC ophthalmology, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jianguo XuCollege of Mechanical & Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, P. R. China. xu_nuaa_edu@hotmail.com.
Jiahao WeiCollege of Mechanical & Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, P. R. China.
Rong TanCollege of Mechanical & Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, P. R. China.
Jianxin ShenCollege of Mechanical & Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, P. R. China.
Jin YaoThe Affiliated Eye Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China.
Fen ZhouThe Affiliated Eye Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China. zf520simple@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate measurement of minimum macular hole (MH) diameter is essential for diagnosis and treatment. The manual measurement approach by ophthalmologists is time-consuming, poorly reproducible, and exhibits high inter-observer variability. Threshold-based methods are sensitive to image quality, and perform inadequately in low-contrast optical coherence tomography (OCT) images. Deep learning can achieve higher measurement accuracy but shows limited generalization capability.

methodsWe propose an automated measurement method comprising three sub-tasks. Specifically, a tailored MH dataset is first created by cropping publicly available OCT images to minimize interference from non-MH regions. Subsequently, an image processing pipeline, which consists of denoising, binarization, morphological operations, and edge detection, is implemented to extract the contours on both sides of MH. Finally, an automated measurement algorithm is designed to locate the closest points on the bilateral contours of MH and thereby calculate the minimum diameter.

resultsExtensive experiments are conducted to validate the effectiveness of this research. More concretely, on the public dataset, the [Formula: see text] metric obtained by the proposed method closely aligns with the [Formula: see text] metric. The ADE values are predominantly within 1 pixel, achieving as low as 0.00 in some cases. Simultaneously, the method demonstrates favorable performance in the RDE metric, with values ranging from 0.00% to 3.29%, most of which remain below 2.50%. Furthermore, in clinical testing, the measurement time per individual sample is approximately 4 seconds, indicating high efficiency.

conclusionsOverall, the comprehensive quantitative and qualitative experimental results confirm the method's commendable accuracy and execution efficiency in measuring the minimum MH diameter, demonstrating its potential value in assisting ophthalmologists with the diagnosis and treatment assessment of MH conditions.

Indexed as

Image Processing, Computer-AssistedRetinal PerforationsTomography, Optical CoherenceAlgorithmsDeep LearningHumansReproducibility of ResultsEdge detectionImage binarizationImage denoisingMacular holeMorphological operation

Identifiers

PMID42177481
PMCPMC13393245

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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.