Intraretinal fluid identification via enhanced maps using optical coherence tomography images

Abstract

Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization. We present a novel methodology to identify these fluid accumulations by means of generating binary maps (offering a direct representation of these areas) and heat maps (containing the region confidence). To achieve this, a set of 312 intensity and texture-based features were studied. The most relevant features were selected using the sequential forward selection (SFS) strategy and tested with three archetypal classifiers: LDC, SVM and Parzen window. Finally, the most proficient classifier is used to create the proposed maps. All of the tested classifiers returned satisfactory results, the best classifier achieving a mean test accuracy higher than 94% in all of the experiments. The suitability of the maps was evaluated in a context of a screening issue with three different datasets obtained with two different devices, testing the capabilities of the system to work independently of the used OCT device. The experiments with the map creation were performed using 323 OCT images. Using only the binary maps, more than 91.33% of the images were correctly classified. With only the heat maps, the proposed methodology correctly separated 93.50% of the images.

Automatic identification and characterization of the epiretinal membrane in OCT images

“…The OCT image modality increased its popularity over the recent years as it offers crosssectional visualizations of the retinal tissues in a non-invasive way [13,14]. OCT scans are frequently used in the detection and analysis of retinal diseases that produce structural alterations of the retina [15] as happens, for example, with macular edemas or cysts [16]. OCT is widely used to detect the presence of the ERM, since its appearance is indicated by a wrinkling on the retinal surface, or as a bright layer superposed with the inner limiting membrane (ILM), the topmost layer of the retina, illustrated in Fig.…”

Automatic Tool for the Detection, Characterization and Intuitive Visualization of Macular Edema Regions in OCT Images

“…Within this region, representative samples were extracted and, after being selected the most relevant features by means of a feature selection strategy, used to train specific models for each type of DME [2]. Finally, the models are used to create intuitive color maps [3] representing the three DME types, thus facilitating the clinical work.…”

Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images

“…To solve this issue, we merged a regional analysis strategy that has proven to be resilient in the identification of fluid regions [1], a visualization technique specially designed to offer satisfactory results even when facing the most challenging conditions [2] and an artificial neural network architecture specially designed to overcome overfitting thanks to its densely connected layers, adding capabilities of self-supervision [3]. Thus, to generate the pathological confidence maps, the images are thoroughly sampled, extracting thousands of samples from them.…”