Automatic Retinal and Choroidal Boundary Segmentation in OCT Images Using Patch-Based Supervised Machine Learning Methods

Alonso‐Caneiro, David; Kugelman, Jason; Hamwood, Jared; Read, Scott A.; Vincent, Stephen J.; Chen, Fred K.; Collins, Michael J.

doi:10.1007/978-3-030-21074-8_17

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…Recently, DL has been successfully applied in analyzing medical images of ultrasound [22,23], CT [24][25][26], and MRI [27][28][29]. In ophthalmology, there is also an emerging study of applying AI to analyze ultrasound images [30], retinal fundus images [31], and OCT [11,15,19,[32][33][34][35][36]. More recently, there are emerging reports of utilizing AI algorithms in OCT images to diseases such as diabetic retinopathy [37], glaucoma [11,38], macular degeneration [36], and retinal detachment [39].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

et al. 2022

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Introduction: Development and validation of a deep learning algorithm to automatedly identify and locate ERM regions in OCT images. Methods: OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images. A total of 422 images (90%) and the rest 46 images (10%) were used as the training dataset and validation dataset for deep learning algorithm training and validation, respectively. One senior and one junior clinician read the images. The diagnostic results were compared. Results: The algorithm accurately segmented and located the ERM regions in OCT images. The image-level accuracy was 95.65%, and the ERM region-level accuracy was 90.14%, respectively. In comparison experiments, the accuracies of the junior clinician improved from 85.00% and 61.29% without the assistance of the algorithm to 100.00% and 90.32% with the assistance of the algorithm. The corresponding results of the senior clinician were 96.15%, 95.00% without the assistance of the algorithm, and 96.15%, 97.50% with the assistance of the algorithm. Conclusions: The developed deep learning algorithm can accurately segmenting ERM regions in OCT images. This deep learning approach may help clinicians in clinical diagnosis with better accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

et al. 2022

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“…Deep learning algorithms, particularly convolutional neural networks (CNN), have been increasingly applied to retinal segmentation in OCT images [28]- [31]. Methods that combine CNN with graph-search algorithms are a common approach to improve the segmentation performance [32]- [34]. Other deep learning approaches to retinal segmentation include recurrent neural networks [35], and fully convolutional networks [36], [37].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

2021

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Manual assessment of the retinal thickness in optical coherence tomography images is a timeconsuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The difficulty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets that included cases of severe retinal edema. Moreover, the performance of the proposed method was compared to two existing methods, widely referenced in the relevant literature. The proposed algorithm outperformed reference methods at segmenting the retinal boundaries in both normal and pathological images. Furthermore, a thorough reliability analysis showed a strong agreement between the retinal thickness measurements derived from the segmentation obtained with the proposed method and corresponding manual measurements computed with the manual annotations. INDEX TERMSDeep learning, ensemble learning, optical coherence tomography (OCT), retinal thickness VOLUME x, 202x

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“…However, deep learning with neural networks, in particular, is frequently used for OCT image segmentation. A number of different methods have been utilized including patch-based classification [24]- [27], semantic segmentation [28]- [34], adversarial learning [35], and transfer learning [36]. Additionally, some methods [37], [38] have used volumetric input data, consisting of multiple image slices instead of a standard single image.…”

Section: Introductionmentioning

confidence: 99%

Effect of Altered OCT Image Quality on Deep Learning Boundary Segmentation

et al. 2020

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Deep learning methods provide a platform to segment boundaries within the retina and choroid in OCT images of the posterior eye, with the ultimate goal of having a robust model that works well across a wide range of different datasets. However, since most studies of deep learning methods use datasets exhibiting similar image quality for both training and evaluation, the effect of varied image quality on such methods is not normally explored in the context of OCT image segmentation. An understanding of the effects of image quality factors is vital to determine the robustness of the methods and their ability to be applied in clinical practice where images exhibiting a range of different qualities are encountered. This study examined a range of factors that can affect standard OCT image quality and determined how and why the performance of an existing neural network based segmentation method can subsequently degrade as a result. Three image quality factors (noise, contrast reduction, and gamma correction) all had a negative impact upon performance, while more robust performance was maintained in the presence of both JPEG and JPEG2000 image compression. Improving the method's robustness to each of these degradations is also demonstrated with marked performance improvements identified by applying a fine-tuning approach to the network. This study improves our understanding of the effect of OCT image degradation on neural network performance, the effect that fine-tuning with poor-image quality data has on the network and highlights the benefit and importance of training resilient models using data augmentation.

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Automatic Retinal and Choroidal Boundary Segmentation in OCT Images Using Patch-Based Supervised Machine Learning Methods

Cited by 12 publications

References 28 publications

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

Effect of Altered OCT Image Quality on Deep Learning Boundary Segmentation

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