Automatic macular edema identification and characterization using OCT images

Samagaio, Gabriela; Estévez, Aída; Moura, Joaquim de; Novo, Jorge; Fernández, M. Isabel; Ortega, Marcos

doi:10.1016/j.cmpb.2018.05.033

Cited by 39 publications

(21 citation statements)

References 68 publications

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“…Among the different retinal image modalities, Optical Coherence Tomography (OCT) is a powerful tool for the diagnosis of retinal diseases, since the image acquisition in these devices consists in a contactless, non-invasive method which gives a set of images of the main retinal structures in real time [5], [6]. Nowadays, OCT imaging can be employed in the analysis and diagnose of many relevant diseases: the information regarding the thickness of OCT layers is useful in the diagnosis of glaucoma [7], macular degeneration [8], [9] or diabetic retinopathy [10], among others. Also, the retinal thinning is studied in multiple sclerosis [11].…”

Section: Introductionmentioning

confidence: 99%

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

González-López

Moura

Novo

et al. 2019

Heliyon

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Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy

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Section: Introductionmentioning

confidence: 99%

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

González-López

Moura

Novo

et al. 2019

Heliyon

Self Cite

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“…The results showed a slight improvement in sensitivity and specificity with similar AUC values. Although OCT could offer non-invasive real-time, high-resolution imaging on the structure of the retina and had become the mainstay of retinal diagnosis in clinical routine, subjective qualitative OCT image evaluation required significant clinical training and agreement was also limited, even among ophthalmologists with rich clinical experience [2,4,12,32]. Furthermore, it was inherently impractical to manually inspect large OCT datasets in busy clinics, and prone to errors [9].…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays, OCT has become a powerful imaging modality for non-invasive assessment of various retinal abnormalities [1][2][3][4][5], such as assisting the diagnosis of CNV, DME, and DRUSEN, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based automated detection of retinal diseases using optical coherence tomography images

Chen

Liu

et al. 2019

Biomed. Opt. Express

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Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for medical applications. This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50). The experiment followed a patient-level 10-fold cross-validation process, on development retinal OCT image dataset. The proposed approach achieved 0.973 (95% confidence interval [CI], 0.971-0.975) classification accuracy, 0.963 (95% CI, 0.960-0.966) sensitivity, and 0.985 (95% CI, 0.983-0.987) specificity at the B-scan level, achieving a matching or exceeding performance to that of ophthalmologists with significant clinical experience. Other performance measures used in the study were the area under receiver operating characteristic curve (AUC) and kappa value. The observations of the study implied that multi-ResNet50 ensembling was a useful technique when the availability of medical images was limited. In addition, we performed qualitative evaluation of model predictions, and occlusion testing to understand the decision-making process of our model. The paper provided an analytical discussion on misclassification and pathology regions identified by the occlusion testing also. Finally, we explored the effect of the integration of retinal OCT images and medical history data from patients on model performance.

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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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Automatic macular edema identification and characterization using OCT images

Cited by 39 publications

References 68 publications

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Deep learning-based automated detection of retinal diseases using optical coherence tomography images

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

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