Joaquim de Moura

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...

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...

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...

Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach

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This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as source of information. To do so, the system extracts a set of deep features using a transfer learning-based approach from different fully-connected layers and different pre-trained Convolutional Neural Network (CNN) models. Next, the most relevant subset of deep features is identified using representative feature selection methods. Finally, a machine learning strategy is applied to train and test the potential of the identified deep features in the pathological classification process. Satisfactory results were obtained, demonstrating the suitability of the presented system to filter those pathological DME cases, helping the specialist to optimize their diagnostic procedures. ( Read Full Article )

Intraretinal fluid identification via enhanced maps using optical coherence tomography images

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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 m...

Automatic Identification of Macular Edema in Optical Coherence Tomography Images

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This paper proposes a novel system for the simultaneous identification and characterization of the three types of Macular Edema (ME) in Optical Coherence Tomography (OCT). These MEs are clinically defined, by the reference classification of the field, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Our system uses multilevel image thresholding approaches to identify the SRD and CME cases and a learning approach for the DRT identification. The system provided promising results with F-Measures of 83.35% and 81.95% for the DRT and CME detections, respectively. It was also efficient in detecting all the SRD cases included in the testing image dataset. The system was able to identify individually the different types of ME on the OCT images but it was also capable to detect simultaneously the existence of the three ME cases when they appeared merged in the lower retinal layers. ( Read Full Article )

Optical Coherence Tomography Denoising by Means of a Fourier Butterworth Filter-Based Approach

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Optical Coherence Tomography (OCT) is affected by ubiquitous speckle noise that difficult the visualization and analysis of the retinal structures. Any denoising strategy should be able to remove efficiently the noise as well as preserves clinical information contained in the images. This information is crucial to analyses the retinal layer tissue that allows the posterior analysis and recognition of relevant diseases as macular edema or diabetic retinopathy. To address this issue, a method based on the Fourier Butterworth filter combined with a contrast enhancement and a histogram regularization was developed in order to reduce the speckle noise in OCT retinal images. The proposed method was validated using 45 OCT retinal images organized into 3 groups of noise degree, comparing the results with the performance of representative methods of the state-of-the-art. The validation and comparison were made through three quantitative metrics: Signal-to-Noise Ratio (SNR), Contrast-to-Noise...

Feature Definition and Selection for Epiretinal Membrane Characterization in Optical Coherence Tomography Images

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Optical Coherence Tomography (OCT) is a common imaging technique for the detection and analysis of optical diseases, since it is a non invasive method that generates in vivo a cross-sectional visualization of the retinal tissues. These characteristics contributed to the use of OCT imaging in the analysis of pathologies as, for instance, vitreomacular traction, age-related macular degeneration or hypertension. Among its applications, OCT imaging can be used in the detection of any present epiretinal membrane section in the retina, a critical issue to prevent further complications caused by this pathology. This work analyzed the main characteristics of the epiretinal membrane to define a complete and heterogeneous set of intensity and texture-based features. Those features were studied using representative selectors, as Correlation Feature Selection (CFS) and Relief-F, to identify the optimal subsets that offer the higher discriminative power. K-Nearest Neighbor (kNN), Naive Bayes and...


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