A Review On digital image processing techniques for in-Vivo confocal images of the cornea

Herrera-Pereda, Raidel; Taboada‐Crispi, Alberto; Babin, Danilo; Philips, Wilfried; Costa, Márcio Holsbach

doi:10.1016/j.media.2021.102188

Cited by 16 publications

(11 citation statements)

References 136 publications

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“…In recent years, several new approaches to estimate the corneal endothelial parameters have been proposed. Up to 2018, the methods were based on image processing techniques and classic machine learning [13][14][15][16][17] , but from 2018 onward, a significantly large number of new approaches based on deep learning have been presented [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] . Overall, these methods have shown a good performance.…”

mentioning

confidence: 99%

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae

Vigueras-Guillén

Rooij

Dooren

et al. 2022

Sci Rep

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Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To estimate the corneal parameters (endothelial cell density [ECD], coefficient of variation [CV], and hexagonality [HEX]), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 [cells/mm$$^{2}$$ 2 ] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX. Compared with Topcon’s built-in software, our error was 3–6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.

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confidence: 99%

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae

Vigueras-Guillén

Rooij

Dooren

et al. 2022

Sci Rep

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“…Quantitative analysis of corneal nerves have been reviewed adequately in three review papers published in 2018 [ 83 ], 2019 [ 84 ] and 2021 [ 85 ]. Petropoulos et al [ 86 ] suggested corneal confocal microscopy (CCM) to be the final step to arrive at conclusions related to peripheral and central neuro-degenerative conditions.…”

Section: Corneal Nerve Imagesmentioning

confidence: 99%

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Prasad

Yegneswaran

2023

J Med Syst

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Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.

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“…The small field of view, the image acquisition area when sampling, quality of the images, and image analysis methods, all introduce biases to the sampling methodology. Further, the time required to analyse the images needs to be shortened before the instrument is efficient in clinical practice (Herrera-Pereda et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Kongsberg Vision Meeting Abstracts 2022

Baraas¹

2022

Scand J Optom Vis Sci

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Kongsberg Vision Meeting 2022: Abstracts Kongsberg Vision Meeting was held at the University of South-Eastern Norway in Kongsberg, for the 14th time, on November 7–9, 2022. The meeting was organised as a three-day meeting with a lighting design dayfollowed by two clinical optometry and vision research days. Rigmor C. Baraas, Sylvia Pont, Helle K. Falkenberg, Vibeke Sundling, Tove Lise Morisbakk, Gro Horgen Vikesdal, Lotte-Guri B. Steen, Trine Langaas, Randi Mork and Are Røysamb organised the three-day meeting. The theme this year was Light & Vision in a Public Health Perspective. Keynote speakers for the lighting design day was Wout van Bommel and for the research day were Bruce Moore and Vibeke Sundling. Lee Turner from the Department for Education, His Majesty’s Government, UK and Mark Mon-Williams, University of Leeds and Bradford Institute of Health Research, UK held a special session on the randomized control trial (RCT) Glasses-in-Classes, a project run in state-funded primary schools based in the Metropolitan area of Bradford (UK) to ensure that children who need eyeglasses both have access to an eye examination as well as two pairs of glasses. Teachers are informed and trained to ensure children who have been prescribed with glasses wear them at school, and that their spare pair is available if they attend school without their home pair. Preliminary results from the RCT show that wearing eyeglasses over one school year significantly improves reading and literacy skills when a child who need eyeglasses wears them as compared with those who need eyeglasses, but do not wear them. The abstracts from the other invited and contributed talks on the different days are presented in the order they were given. Received October 1, 2022. Accepted November 1, 2022.

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A Review On digital image processing techniques for in-Vivo confocal images of the cornea

Cited by 16 publications

References 136 publications

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Kongsberg Vision Meeting Abstracts 2022

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