ArticleBiomedical optics express2018
Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Macular Layer Thickness with Spectral-Domain and High-Resolution OCT.Ophthalmology science · 2026Article
- Automated Segmentation and Characterization of Retinal Hyperreflective Foci in Age-Related Macular Degeneration.Translational vision science & technology · 2026Article
- Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour segmentation.Scientific reports · 2024Article
- Siamese Unet Network for Waterline Detection and Barrier Shape Change Analysis from Long-Term and Large Numbers of Satellite Imagery.Sensors (Basel, Switzerland) · 2023Article
- Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models.Plant methods · 2023Article
- Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2023Article
- AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.Acta neuropathologica communications · 2023Article
- FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.Sensors (Basel, Switzerland) · 2022Article
- A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation.Scientific reports · 2022Article
- Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.Journal of imaging · 2022Article
- Automated 2D Slice-Based Skull Stripping Multi-View Ensemble Model on NFBS and IBSR Datasets.Journal of digital imaging · 2022Article
- OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN.Sensors (Basel, Switzerland) · 2022Article
- Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images.Translational vision science & technology · 2022Article
- Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator.Scientific reports · 2022Article
- Retinal thickness in healthy Australian Aboriginal and Torres Strait Islander children.PloS one · 2022Article
- Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy.Scientific reports · 2021Article
- Corneal pachymetry by AS-OCT after Descemet's membrane endothelial keratoplasty.Scientific reports · 2021Article
- Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.PloS one · 2021Article
- DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.Translational vision science & technology · 2020Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Deep learning strategies, particularly convolutional neural networks (CNNs), are especially suited to finding patterns in images and using those patterns for image classification. The method is normally applied to an image patch and assigns a class weight to the patch; this method has recently been used to detect the probability of retinal boundary locations in OCT images, which is subsequently used to segment the OCT image using a graph-search approach. This paper examines the effects of a number of modifications to the CNN architecture with the aim of optimizing retinal layer segmentation, specifically the effect of patch size as well as the network architecture design on CNN performance and subsequent layer segmentation. The results demonstrate that increasing patch size can improve the performance of the classification and provides a more reliable segmentation in the analysis of retinal layer characteristics in OCT imaging. Similarly, this work shows that changing aspects of the CNN network design can also significantly improve the segmentation results. This work also demonstrates that the performance of the method can change depending on the number of classes (i.e. boundaries) used to train the CNN, with fewer classes showing an inferior performance due to the presence of similar image features between classes that can trigger false positives. Changes in the network (patch size and or architecture) can be applied to provide a superior segmentation performance, which is robust to the class effect. The findings from this work may inform future CNN development in OCT retinal image analysis.
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