Marcos Ortega

Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images

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Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0...

A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets

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In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expe...

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images

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Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local...

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA image

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Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local...

Fully-Automatic 3D Intuitive Visualization of Age-Related Macular Degeneration Fluid Accumulations in OCT Cubes

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Age-related macular degeneration is the leading cause of vision loss in developed countries, and wet-type AMD requires urgent treatment and rapid diagnosis because it causes rapid irreversible vision loss. Currently, AMD diagnosis is mainly carried out using images obtained by optical coherence tomography. This diagnostic process is performed by human clinicians, so human error may occur in some cases. Therefore, fully automatic methodologies are highly desirable adding a layer of robustness to the diagnosis. In this work, a novel computer-aided diagnosis and visualization methodology is proposed for the rapid identification and visualization of wet AMD. We adapted a convolutional neural network for segmentation of a similar domain of medical images to the problem of wet AMD segmentation, taking advantage of transfer learning, which allows us to work with and exploit a reduced number of samples. We generate a 3D intuitive visualization where the existence, position and severity of t...

End-to-end multi-task learning approaches for the joint epiretinal membrane segmentation and screening in OCT images

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Background and objectives: The Epiretinal Membrane (ERM) is an ocular disease that can cause visual distortions and irreversible vision loss. Patient sight preservation relies on an early diagnosis and on determining the location of the ERM in order to be treated and potentially removed. In this context, the visual inspection of the images in order to screen for ERM signs is a costly and subjective process. Methods: In this work, we propose and study three end-to-end fully-automatic approaches for the simultaneous segmentation and screening of ERM signs in Optical Coherence Tomography images. These convolutional approaches exploit a multi-task learning context to leverage inter-task complementarity in order to guide the training process. The proposed architectures are combined with three different state of the art encoder architectures of reference in order to provide an exhaustive study of the suitability of each of the approaches for these tasks. Furthermore, these architectures w...

Feature definition and comprehensive analysis on the robust identification of intraretinal cystoid regions using optical coherence tomography images

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Currently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the devi...

Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms

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Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal pathologies. Although each of them has its advantages and disadvantages, the joint analysis of the images produced by both techniques to analyze a specific area of the retina is of increasing interest, given that they provide common and complementary visual information. However, in order to facilitate this analysis task, a previous registration of the pair of FA and OCTA images is desirable in order to superimpose their common areas and focus the gaze on the regions of interest. Normally, this task is manually carried out by the expert clinician, but it turns out to be tedious and time-consuming. Here, we present a three-stage methodology for robust multimodal registration of FA and superficial plexus OCTA images. The first one is a preprocessing stage devoted to reducing the n...

Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membranein 3D OCT Images Using Deep Convolutional Approaches

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Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence Tomography (OCT) volumes. The proposed system uses a convolutional neural network architecture to classify patches of the retina surface. All the 2D OCT slices of the 3D OCT volume of a patient are combined to produce an intuitive colour map over the 2D fundus reconstruction, providing a visual representation of the presence of ERM which therefore facilitates the diagnosis and...

Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images

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The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identifica...

Intraretinal fluid map generation in optical coherence tomography images (book chapter)

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The retina represents one of the most studied parts of the human body, thanks to its easy access for any ophthalmological study. It represents the main neurosensory part of the eye and, by its study, pathologies not only from the visual system but also from different body systems (like the neural system or vascular system) can be detected. Among the principal medical imaging modalities that are used to study the eye fundus and the retinal histological structures, the optical coherence tomography (OCT) has proven to be one of the most reliable techniques. It is widely used for the detection of diseases like the diabetic macular edema and the age-related macular degeneration, which cause fluid leakages between the retinal layers. Both these diseases are among the main causes of blindness in developed countries, as these leakages tear the retinal structure and provoke the formation of other pathological bodies. As the symptoms start to be noticeable only when they cause a severe amount...

Intuitive and Coherent Intraretinal Cystoid Map Representation in Optical Coherence Tomography Images

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Fluid accumulations in between the retinal layers represent one of the main causes of blindness in developed countries. Currently, these fluid accumulations are detected by means of a manual inspection of Optical Coherence Tomography images, prone to subjective and non-quantifiable diagnostics. For this reason, numerous works aimed for an automated methodology. Nonetheless, these systems mostly focus on obtaining a defined segmentation, which is not always possible. For this reason, we present in this work a fully automatic methodology based in a fuzzy and confidence-based visualization of a regional analysis, allowing the clinicians to study the fluid accumulations independently of their distribution, complications and/or other artifacts that may complicate the identification process. ( Read Full Article )

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

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Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cr...

Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes

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Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphologic...

A Novel Automatic Method to Estimate Visual Acuity and Analyze the Retinal Vasculature in Retinal Vein Occlusion Using Swept Source Optical Coherence Tomography Angiography

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The assessment of vascular biomarkers and their correlation with visual acuity is one of the most important issues in the diagnosis and follow-up of retinal vein occlusions (RVOs). The high workloads of clinical practice make it necessary to have a fast, objective, and automatic method to analyze image features and correlate them with visual function. The aim of this study is to propose a fully automatic system which is capable of estimating visual acuity (VA) in RVO eyes, based only on information obtained from macular optical coherence tomography angiography (OCTA) images. We also propose an automatic methodology to rapidly measure the foveal avascular zone (FAZ) area and the vascular density (VD) in the superficial and deep capillary plexuses in swept-source OCTA images centered on the fovea. The proposed methodology is validated using a representative sample of 133 visits of 50 RVO patients. Our methodology estimates VA with very high precision and is even more accurate when we ...

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