Image based analysis of meibomian gland dysfunction using conditional generative adversarial neural network

Abstract

ObjectiveMeibomian gland dysfunction (MGD) is a primary cause of dry eye disease. Analysis of MGD, its severity, shapes and variation in the acini of the meibomian glands (MGs) is receiving much attention in ophthalmology clinics. Existing methods for diagnosing, detection and analysing meibomianitis are not capable to quantify the irregularities to IR (infrared) images of MG area such as light reflection, interglands and intraglands boundaries, the improper focus of the light and positioning, and eyelid eversion.Methods and analysisWe proposed a model that is based on adversarial learning that is, conditional generative adversarial network that can overcome these blatant challenges. The generator of the model learns the mapping from the IR images of the MG to a confidence map specifying the probabilities of being a pixel of MG. The discriminative part of the model is responsible to penalise the mismatch between the IR images of the MG and confidence map. Furthermore, the adversarial learning assists the generator to produce a qualitative confidence map which is transformed into binary images with the help of fixed thresholding to fulfil the segmentation of MG. We identified MGs and interglands boundaries from IR images.ResultsThis method is evaluated by meiboscoring, grading, Pearson correlation and Bland-Altman analysis. We also judged the quality of our method through average Pompeiu-Hausdorff distance, and Aggregated Jaccard Index.ConclusionsThis technique provides a significant improvement in the quantification of the irregularities to IR. This technique has outperformed the state-of-art results for the detection and analysis of the dropout area of MGD.

Deepfakes in Ophthalmology

“…We found 20 published implementations of GANs specific to ophthalmology. Of these, 11 manuscripts synthesized fundus images, 9,19,20,23,32,37,[40][41][42][43][44] 6 manuscripts synthesized OCT images, [27][28][29][45][46][47] 2 manuscripts synthesized fluorescein angiography images, 48,49 and 1 manuscript synthesized infrared images 21 (Table 2). The majority of GANs were proof-ofconcept studies demonstrating feasibility of generating realistic-appearing synthetic images, specific implementations of GANs were published in 9 for diagnosis of ophthalmic diseases, including diabetic retinopathy (DR), 9,20,32,40 glaucoma, 28,45 age-related macular degeneration (AMD), 19,46 and meibomian gland dysfunction.…”

“…16 Deepfakes have garnered notoriety in the media for their nefarious applications, 17,18 but recently have been explored in multiple medical domains. 9,[19][20][21][22][23][24][25][26] Since ophthalmology has been at the forefront of the DL revolution, there are numerous potential applications of synthetic images, starting with fundus 9,19,20 and optical coherence tomography (OCT). [27][28][29] Synthetic images can be modified to adjust image features such as pigmentation, 9 image quality, 30 and even disease severity.…”

Artificial Intelligence in Dry Eye Disease

“…Deep learning approaches were used by four studies evaluating meibomian gland features [46,39,33,31].…”

“…This is an example of uncommonly good practice, as most medical AI systems are developed and evaluated on data from only one device and/or hospital. The only study to use a GAN architecture tested it on infrared 3D images of meibomian glands in order to evaluate MGD [31]. Comparing the model output with true labels, the performance scores were better than for state of the art segmentation methods.…”

Meibomian Glands Segmentation In Near-Infrared Images With Weakly Supervised Deep Learning

“…Prabhu et al [10] used fine-tuning U-Net for the automatic segmentation of meibomian glands. Khan et al [11] proposed an adversarial learning based method. However, there are not many methods for automatic segmentation of meibomian glands.…”