An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images

Sidib, Dsir; Sankar, Shrinivasan; Lematre, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Cheung, Carol Y.; Tan, Gavin Siew Wei; Miléa, Dan; Lamoureux, Ecosse L.; Wong, Tien Yin; Mriaudeau, Fabrice

doi:10.1016/j.cmpb.2016.11.001

Cited by 56 publications

(26 citation statements)

References 21 publications

(25 reference statements)

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“…Sidibe et al [ 20 ] proposed a classification model for DME patients by modelling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume was based on the number of detected outliers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Using these four sources of images, we constructed seven datasets see Table 1 . D1 is constructed with normal and DME subjects because Venhuizen et al [ 19 ], Lemaitre et al [ 2 ] and Sidibe et al [ 20 ] report their methods performance on this partial data of Duke University. The datasets is constructed to show the performance of our proposed method in different sources of images and combine sources.…”

Section: Dataset and Experiments Setupmentioning

confidence: 99%

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Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm

et al. 2018

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In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Dataset and Experiments Setupmentioning

confidence: 99%

Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm

et al. 2018

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“…Advances in the diagnosis and understanding of the DME disease have been made in the recent decades [6], including the proposal of new therapies besides macular laser therapy. Also, retinal image analysis using Optical Coherence Tomography (OCT) scans became popular for the pathological DME identification [7] and characterization using both traditional classification techniques [8] or more novel ones, such as deep learning strategies [9,10]. Research in these scenarios contributes to the improvement of diagnosis, prognosis and monitoring tasks of the DME disease.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification and characterization of the epiretinal membrane in OCT images

Baamonde

Moura

Novo

et al. 2019

Biomed. Opt. Express

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Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%.

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“…A typical ophthalmological examination of the retina may include an analysis of eye fundus images and in some cases SD-OCT to locate retinal vascular damage and changes in choroidal thickness [6]. The DR and DME diagnoses are performed by looking for the presence of microaneurysms, intraretinal hemorrhages, exudates and edema [7,8,9]. The evaluation of the thickness of the neurosensory retina, retinal pigment epithelium, and choroid are analyzed independently for the AMD diagnosis [10,11].…”

Section: Introductionmentioning

confidence: 99%

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

Perdomo

Ríos

Rodríguez

et al. 2019

Computer Methods and Programs in Biomedicine

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Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal diseases.Methods: This article presents a new deep learning model, OCT-NET, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. OCT-NET is applied to the classification of three conditions seen in SD-OCT volumes. Additionally, the proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. Results: The proposed model was tested on the public SERI-CUHK and A2A SD-OCT data sets containing healthy, diabetic retinopathy, diabetic macular edema and age-related macular degeneration. The experimental evaluation shows that the proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the SERI+CUHK and A2A SD-OCT data sets with a precision of 93% and an area under the ROC curve (AUC) of 0.99 respectively. Conclusions:The proposed method is able to classify the three studied retinal diseases with high accuracy. One advantage of the method is its ability to produce interpretable clinical information in the form of highlighting the regions of the image that most contribute to the classifier decision.

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An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images

Cited by 56 publications

References 21 publications

Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm

Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm

Automatic identification and characterization of the epiretinal membrane in OCT images

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

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