Retinal Fluids Segmentation Using Volumetric Deep Neural Networks on Optical Coherence Tomography Scans

2020 ◽  
Author(s):  
K. Alsaih ◽  
M. Z. Yusoff ◽  
T. B. Tang ◽  
I. Faye ◽  
F. Meriaudeau
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Deep Neural Networks ◽  
Optical Coherence

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

2021 ◽  
pp. 104822
Author(s):  
Josef Kauer-Bonin ◽  
Sunil K. Yadav ◽  
Ingeborg Beckers ◽  
Kay Gawlik ◽  
Seyedamirhosein Motamedi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Control ◽  
Optical Coherence Tomography ◽  
Deep Neural Networks ◽  
Optical Coherence ◽  
Automatic Quality Control

scholarly journals Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7521
Author(s):  
Agnieszka Stankiewicz ◽  
Tomasz Marciniak ◽  
Adam Dabrowski ◽  
Marcin Stopa ◽  
Elzbieta Marciniak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Deep Neural Networks ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Optical Coherence ◽  
The Neural Network ◽  
Inner Limiting Membrane ◽  
Segmentation Quality ◽  
Oct Imaging

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

scholarly journals Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 51 ◽  
Author(s):  
Qingge Ji ◽  
Jie Huang ◽  
Wenjie He ◽  
Yankui Sun
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Natural Images ◽  
Small Scale ◽  
Optical Coherence ◽  
Deep Convolutional Neural Networks ◽  
Macular Diseases ◽  
High Level

Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.

scholarly journals Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yu Fujinami-Yokokawa ◽  
Nikolas Pontikos ◽  
Lizhu Yang ◽  
Kazushige Tsunoda ◽  
Kazutoshi Yoshitake ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Test Accuracy ◽  
Test Sets ◽  
The Mean ◽  
Per Gene ◽  
Sd Oct ◽  
Gene Category

Purpose. To illustrate a data-driven deep learning approach to predicting the gene responsible for the inherited retinal disorder (IRD) in macular dystrophy caused by ABCA4 and RP1L1 gene aberration in comparison with retinitis pigmentosa caused by EYS gene aberration and normal subjects. Methods. Seventy-five subjects with IRD or no ocular diseases have been ascertained from the database of Japan Eye Genetics Consortium; 10 ABCA4 retinopathy, 20 RP1L1 retinopathy, 28 EYS retinopathy, and 17 normal patients/subjects. Horizontal/vertical cross-sectional scans of optical coherence tomography (SD-OCT) at the central fovea were cropped/adjusted to a resolution of 400 pixels/inch with a size of 750 × 500 pix2 for learning. Subjects were randomly split following a 3 : 1 ratio into training and test sets. The commercially available learning tool, Medic mind was applied to this four-class classification program. The classification accuracy, sensitivity, and specificity were calculated during the learning process. This process was repeated four times with random assignment to training and test sets to control for selection bias. For each training/testing process, the classification accuracy was calculated per gene category. Results. A total of 178 images from 75 subjects were included in this study. The mean training accuracy was 98.5%, ranging from 90.6 to 100.0. The mean overall test accuracy was 90.9% (82.0–97.6). The mean test accuracy per gene category was 100% for ABCA4, 78.0% for RP1L1, 89.8% for EYS, and 93.4% for Normal. Test accuracy of RP1L1 and EYS was not high relative to the training accuracy which suggests overfitting. Conclusion. This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD-OCT, with a high prediction accuracy. It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.

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