Automatic intraocular lens segmentation and detection in optical coherence tomography images

Gillner, M; Eppig, Timo; Langenbucher, Achim

doi:10.1016/j.zemedi.2013.07.002

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Different to the IOL, the retrolental space and BS have never before been segmented in an automated way and are much harder to identify and segment due to their irregular and strongly varying appearance (Gillner et al 2014;Xin et al 2020). This is For each metric the mean and the standard deviation are provided, where the latter is provided in parentheses.…”

Section: Discussionmentioning

confidence: 99%

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Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

Seeböck

Schartmüller

Leydolt

et al. 2022

Acta Ophthalmologica

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Purpose To develop and validate a deep learning model to automatically segment three structures using an anterior segment optical coherence tomography (AS‐OCT): The intraocular lens (IOL), the retrolental space (IOL to the posterior lens capsule) and Berger's space (BS; posterior capsule to the anterior hyaloid membrane). Methods An artificial intelligence (AI) approach based on a deep learning model to automatically segment the IOL, the retrolental space, and BS in AS‐OCT, was trained using annotations from an experienced clinician. The training, validation and test set consisted of 92 cross‐sectional OCT slices, acquired in 47 visits from 41 eyes. Annotations from a second experienced clinician in the test set were additionally evaluated to conduct an inter‐reader variability analysis. Results The AI model achieved a Precision/Recall/Dice score of 0.97/0.90/0.93 for IOL, 0.54/0.65/0.55 for retrolental space, and 0.72/0.58/0.59 for BS. For inter‐reader variability, Precision/Recall/Dice values were 0.98/0.98/0.98 for IOL, 0.74/0.59/0.62 for retrolental space, and 0.58/0.57/0.57 for BS. No statistical differences were observed between the automated algorithm and the inter‐reader variability for BS segmentation. Conclusion The deep learning model allows for fully automatic segmentation of all investigated structures, achieving human‐level performance in BS segmentation. We, therefore, expect promising applications of the algorithm with particular interest in BS in automated big data analysis and real‐time intra‐operative support in ophthalmology, particularly in conjunction with primary posterior capsulotomy in femtosecond laser‐assisted cataract surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

Seeböck

Schartmüller

Leydolt

et al. 2022

Acta Ophthalmologica

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“…83 It is not surprising that the application of ML for anterior segment diseases will become a frequent modality of ophthalmic settings. 60,84,85,86 Given the prevalent of cataract in society, the automatic recognition will be rapid, cost-effective and more reliable.…”

Section: Discussionmentioning

confidence: 99%

Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology

2019

Asia-Pacific Journal of Ophthalmology

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The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML) and deep learning (DL) technologies recently. There are many innovations concerning novel automated technologies including unmanned plane, autonomous vehicles, face and image recognition, automatic speech recognition, natural language processing, bioinformatics, military settings, drug discovery and toxicology. AI is a multi-dimensional technology with various components such as advanced algorithms, ML and DL. AI, ML, and DL are expected to provide recent automated devices to ophthalmologists for early diagnosing and treating ocular disorders in the near future. In fact, AI, ML, and DL are being used in ophthalmic setting to validate the diagnosis of diseases, read images, corneal topographic mapping, and IOL calculations. Diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are the three most common causes of irreversible blindness on a global scale. Ophthalmic imaging provides a way to diagnose and objectively detect the progression of a number of pathologies including DR, AMD, glaucoma and other ophthalmic disorders. There are two methods of imaging generally used as diagnostic methods in ophthalmic practice, including fundus digital photography and optical coherence tomography (OCT). Particularly, OCT has become the most widely used practical imaging modality in ophthalmology settings across developed world. Changes in population demographics and life style, extension of average lifespan, and the changing pattern of chronic diseases, such as obesity, diabetes mellitus (DM), DR, AMD, and glaucoma create a rising demand for such images. Furthermore, the limitation of availability of retina specialists and trained human graders is a major problem in many countries. Consequently, given the current population growth trends, it is inevitable that analysing of such images is time consuming, costly, and prone to human error. Therefore, the detection and treatment of DR, AMD, glaucoma and other ophthalmic disorders will be inevitable through unmanned automated applications system in the near future. Namely, these challenges may be only resolved through unmanned automated applications of novel analysis methods without the need of ophthalmologists, retina specialists and human graders. We therefore provide an overview of the potential effects of the current AI, ML and DL methods and their applications on the early detection and treatment of DR, AMD, glaucoma and other ophthalmic diseases.

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“…Modern optical measurement techniques such as high-resolution anterior segment optical coherence tomographers (OCT) are capable of measuring the axial position (front and back apex position), decentration and tilt of the crystalline lens/IOL before/after cataract surgery [ 12 ]. With a pupil size of around 3 mm or more, novel plug-in software tools (e.g.…”

Section: Introductionmentioning

confidence: 99%

Prediction of IOL decentration, tilt and axial position using anterior segment OCT data

Langenbucher,

Szentmáry,

Cayless

et al. 2023

Graefes Arch Clin Exp Ophthalmol

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Background Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures. Methods Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data. Results In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confidence intervals, decentration/tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without significance for decentration and tilt. Conclusion Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with ‘real’ IOL positions and for indications for premium lenses, for which misplacement is known to induce photic effects and image distortion.

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Automatic intraocular lens segmentation and detection in optical coherence tomography images

Cited by 10 publications

References 23 publications

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology

Prediction of IOL decentration, tilt and axial position using anterior segment OCT data

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