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.

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