Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling

Islam, Mohammad Shafkat; Wang, Jui-Kai; Deng, Wenxiang; Thurtell, Matthew J.; Kardon, Randy H.; Garvin, Mona K.

doi:10.1007/978-3-030-63419-3_14

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pekala et al 35 proposed another OCT segmentation method using a combination of fully convolutional networks based on DenseNet and Gaussian process regression. Islam et al 36 proposed to use a variant of feature pyramid network to obtain total-retinal thickness maps from 2D color fundus photographs.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning–Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Liu

Tao

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT). Methods:We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated. Results:The proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values. Conclusions:Experimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation.Translational Relevance: Automated segmentation using a deep learning-based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning–Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Liu

Tao

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

show abstract

“…Deep-learning approaches 24 – 26 have rapidly developed and been increasingly applied to solve problems in neuro-ophthalmology, 27 including detection and quantification of papilledema severity from fundus photographs. 28 – 31 Variational autoencoders (VAEs) 32 – 34 are a well-known generative deep-learning architecture used to synthesize new data associated with the probability distribution of the training data and to enable visualization of smooth morphing between any two data points. A typical VAE concatenates an encoder and a decoder: The encoder is trained to deconstruct the input (e.g., images) into a succinct numeric representation, referred to as “latent variables,” and the decoder is simultaneously trained to reconstruct the input only based on these latent variables.…”

Section: Introductionmentioning

confidence: 99%

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders

Wang,

Linton,

Johnson

et al. 2024

Trans. Vis. Sci. Tech.

Self Cite

View full text Add to dashboard Cite

Purpose To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment. Methods A novel bi-channel deep-learning variational autoencoder (biVAE) model was trained using 1498 optical coherence tomography (OCT) scans of 125 subjects over time from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and 791 OCT scans of 96 control subjects from the University of Iowa. An independent test dataset of 70 eyes from 70 papilledema subjects was used to evaluate the ability of the biVAE model to quantify and reconstruct the papilledema spatial patterns from input OCT scans using only two variables. Results The montage color maps of the retinal nerve fiber layer (RNFL) and total retinal thickness (TRT) produced by the biVAE model provided an organized visualization of the variety of morphological patterns of optic disc edema (including differing patterns at similar thickness levels). Treatment effects of acetazolamide versus placebo in the IIHTT were also demonstrated in the latent space. In image reconstruction, the mean signed peripapillary retinal nerve fiber layer thickness (pRNFLT) difference ± SD was −0.12 ± 17.34 µm, the absolute pRNFLT difference was 13.68 ± 10.65 µm, and the RNFL structural similarity index reached 0.91 ± 0.05. Conclusions A wide array of structural patterns of papilledema, integrating the magnitude of disc edema with underlying disc and retinal morphology, can be quantified by just two latent variables. Translational Relevance A biVAE model encodes structural patterns, as well as the correlation between channels, and may be applied to visualize individuals or populations with papilledema throughout treatment.

show abstract

Classifying and quantifying changes in papilloedema using machine learning

Branco,

Wang,

Elze

et al. 2024

BMJ Neurol Open

Self Cite

View full text Add to dashboard Cite

BackgroundMachine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify papilloedema and detect a treatment effect on papilloedema due to idiopathic intracranial hypertension.MethodsWe trained a convolutional neural network to assign a Frisén grade to fundus photos taken from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT). We applied modified subject-based fivefold cross-validation to grade 2979 longitudinal images from 158 participants’ study eyes (ie, the eye with the worst mean deviation) in the IIHTT. Compared with the human expert-determined grades, we hypothesise that ML-estimated grades can also demonstrate differential changes over time in the IIHTT study eyes between the treatment (acetazolamide (ACZ) plus diet) and placebo (diet only) groups.FindingsThe average ML-determined grade correlated strongly with the reference standard (r=0.76, p<0.001; mean absolute error=0.54). At the presentation, treatment groups had similar expert-determined and ML-determined Frisén grades. The average ML-determined grade for the ACZ group (1.7, 95% CI 1.5 to 1.8) was significantly lower (p=0.0003) than for the placebo group (2.3, 95% CI 2.0 to 2.5) at the 6-month trial outcome.InterpretationSupervised ML of fundus photos quantified the degree of papilloedema and changes over time reflecting the effects of ACZ. Given the increasing availability of fundus photography, neurologists will be able to use ML to quantify papilloedema on a continuous scale that incorporates the features of the Frisén grade to monitor interventions.

show abstract

Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling

Cited by 3 publications

References 11 publications

Deep Learning–Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Deep Learning–Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders

Classifying and quantifying changes in papilloedema using machine learning

Contact Info

Product

Resources

About