• Glaucoma is one of the leading causes of irreversible blindness worldwide. • Diagnosing it in highly myopic patients is challenging due to anatomical alterations. • The domain-specific transfer learning method proposed improves model generalization. • Helps differentiate true glaucomatous patterns from high myopia structural changes. • First designed to detect glaucoma in highly myopic patients using only fundus images. Glaucoma is a leading cause of irreversible blindness worldwide, with early detection being essential to preventing vision loss. However, diagnosing glaucoma in highly myopic patients poses significant challenges due to anatomical alterations, such as optic nerve head deformation and retinal nerve fiber layer thinning, potentially obscuring key disease features and causing misdiagnosis. To address these limitations, we propose a novel deep learning-based framework for automated glaucoma screening in highly myopic eyes. Our approach leverages multi-transfer learning, integrating large-scale pretraining with domain-specific adaptation using ophthalmic disease datasets. This methodology enables the model to extract robust and highly discriminative features, improving sensitivity to glaucomatous changes in myopic eyes. Additionally, we incorporate domain transfer pipeline to address the distributional differences between standard datasets and myopia-specific cases, further enhancing the model’s generalization capabilities. To rigorously evaluate our approach, we conduct a comprehensive analysis of state-of-the-art architectures and transfer learning strategies, assessing their impact on classification performance. Experimental results demonstrate that the proposed model consistently outperforms baseline methods, achieving superior accuracy and robustness in glaucoma detection within highly myopic populations. These findings underscore the potential of AI-driven screening tools as reliable and accurate diagnostic aids, supporting clinicians in the early detection and effective management of glaucoma in complex cases.

Essential tremor (ET) is a prevalent movement disorder characterized by motor and non-motor symptoms, often associated with neurodegeneration. Optical coherence tomography (OCT) has emerged as a valuable tool to identify retinal biomarkers in ET patients. This study presents a novel methodology for ET detection using 3D point clouds derived from retinal OCT layers. Leveraging advanced geometric deep learning (GDL) architectures, including PointTransformer, PointCNN, PointNet++ and SplineCNN, we evaluated the diagnostic potential of individual retinal layers, including the Retinal Nerve Fiber Layer (RNFL), Ganglion Cell Layer (GCL) and Bruch’s Membrane (BM), as well as their combined representation. Our approach achieved state-of-the-art results, with PointTransformer obtaining an F1-score of 0.85 using only BM retinal surface, while requiring just 2% of the original point cloud size. These findings underscore the diagnostic value of OCT-derived 3D data and demonstrate the potential of GDL for computational biomarker extraction in neurodegenerative disorders, offering a scalable and efficient framework for ET diagnosis.

The automatic identification and segmentation of the Foveal Avascular Zone (FAZ) is crucial for diagnosing and monitoring retinal diseases. However, the limited availability of Optical Coherence Tomography Angiography (OCTA) images with ground truth annotations poses a significant challenge for developing robust deep learning models. Traditional transfer learning techniques, such as ImageNet-based pre-training, require large datasets and struggle to adapt to domain-specific tasks in medical imaging. To address this issue, we propose a novel multi-deep transfer learning framework that leverages pretrained models on a generation task using both superficial (SCP) and deep capillary plexuses (DCP) of the retina. To the best of our knowledge, this is the first approach that integrates depth information from multiple vascular plexuses, allowing the model to capture cross-plexus structural relationships and enhance its ability to learn domain-specific vascular features while mitigating data scarcity constraints. We validated our framework through extensive experiments, demonstrating competitive or even superior segmentation performance compared to state-of-the-art pretraining models, despite using significantly smaller datasets. Our approach achieved a Dice coefficient score of 0.8238 ± 0.0263, a Jaccard Index of 0.7189 ± 0.0293, and a Correlation Index of 0.8328 ± 0.0229, highlighting the effectiveness of our multi-depth feature learning strategy in improving segmentation precision and generalization, even with limited data availability.

The registered version of this article, first published in “Pattern Recognition Letters 189, 31-37, 2025", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.patrec.2025.01.003 La versión registrada de este artículo, publicado por primera vez en “Pattern Recognition Letters 189, 31-37, 2025.", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.patrec.2025.01.003

Multiple Sclerosis (MS), the leading cause of non-traumatic neurological impairment in young adults, manifests morphological changes in the retina observable in Optical Coherence Tomography (OCT) images. These changes in the Retinal Nerve Fibre Layer (RNFL) and the Ganglion Cell Layer - Bruch’s Membrane (GCL-BM) serve as potential computational biomarkers for MS. In this work, we propose a transformer-based graph learning approach for analyzing 3D point clouds generated from RNFL and GCL-BM contours, marking a first in the application of geometric deep learning (GDL) to MS diagnosis via OCT scans. Our proposal, tailored for efficiency, synergizes the global contextual strengths of transformers with the detailed, structure-aware capabilities of graph neural networks. Such integration allows for the nuanced analysis of complex retinal structures, significantly boosting the precision of MS detection by uncovering patterns not discernible to the human eye. Additionally, we conducted a comprehensive study on the optimal downsampling size of input 3D point clouds, ensuring efficient data processing without compromising diagnostic accuracy. Our optimal configuration achieved a test F1-Score of 0.88, using only 4.0% of total 3D points, showcasing the effectiveness of our method despite the higher computational demands compared to less complex, albeit less precise, configurations. These promising results are the first in the study of 3D analysis and transformer-based geometric deep learning for MS screening based on OCT images, which are revolutionizing neurophtalmological research.

Drusen, small lipid deposits located below the retina, are early biomarkers of age-related macular degeneration (AMD), a condition that leads to visual impairment worldwide, especially among the elderly. The presence of AMD is linked with Alzheimer’s Disease (AD) and dense deposit disease (DDD), emphasizing the critical need for early and accurate detection of drusen in retinal tissues. Optical Coherence Tomography (OCT), with its non-invasive and high-resolution imaging capabilities, stands as a pivotal tool for early AMD diagnosis through the identification of drusen. However, the reliance on manual segmentation of drusen in 3D OCT images introduces significant challenges: it is not only time-consuming but also subject to inter-observer variability, severely constraining its efficacy for widespread screening applications. These limitations underscore the critical need for an automated solution that can improve diagnostic workflows, ensure consistency across interpretations, and facilitate the processing of large datasets with improved accuracy and efficiency. In response, we propose a new deep learning-based, fully-automatic end-to-end approach for the segmentation of drusen in 3D OCT volumes. Complementary, a pivotal aspect of our research involves the first detailed comparative analysis between 2D and 3D end-to-end approaches. This comparison is crucial for understanding the impact of dimensional spatial information on the accuracy of drusen segmentation within OCT scans. The findings demonstrate that the 3D approach, by leveraging the depth and complexity of spatial data available in 3D OCT volumes, markedly surpasses the 2D approach. This superior performance underlines the importance of 3D spatial information in enhancing diagnostic precision. Through automating the segmentation process, our proposal not only makes AMD screening more efficient and precise but also significantly advances the diagnosis of retinal diseases, potentially enriching our understanding of systemic conditions connected through retinal biomarkers.

The prevalence of neurodegenerative diseases (NDDs) such as Alzheimer's (AD), Parkinson's (PD), Essential tremor (ET), and Multiple Sclerosis (MS) is increasing alongside the aging population. Recent studies suggest that these disorders can be identified through retinal imaging, allowing for early detection and monitoring via Optical Coherence Tomography (OCT) scans. This study is at the forefront of research, pioneering the application of multi-view OCT and 3D information to the neurological diseases domain. Our methodology consists of two main steps. In the first one, we focus on the segmentation of the retinal nerve fiber layer (RNFL) and a class layer grouping between the ganglion cell layer and Bruch's membrane (GCL-BM) in both macular and optic disc OCT scans. These are the areas where changes in thickness serve as a potential indicator of NDDs. The second phase is to select patients based on information about the retinal layers. We explore how the integration of both views (macula and optic disc) improves each screening scenario: Healthy Controls (HC) vs. NDD, AD vs. NDD, ET vs. NDD, MS vs. NDD, PD vs. NDD, and a final multi-class approach considering all four NDDs. For the segmentation task, we obtained satisfactory results for both 2D and 3D approaches in macular segmentation, in which 3D performed better due to the inclusion of depth and cross-sectional information. As for the optic disc view, transfer learning did not improve the metrics over training from scratch, but it did provide a faster training. As for screening, 3D computational biomarkers provided better results than 2D ones, and multi-view methods were usually better than the single-view ones. Regarding separability among diseases, MS and PD were the ones that provided better results in their screening approaches, being also the most represented classes. In conclusion, our methodology has been successfully validated with an extensive experimentation of configurations, techniques and OCT views, becoming the first multi-view analysis that merges data from both macula-centered and optic disc-centered perspectives. Besides, it is also the first effort to examine key retinal layers across four major NDDs within the framework of pathological screening.

Optical Coherence Tomography (OCT) is the major diagnostic tool for the leading cause of blindness in developed countries: Diabetic Macular Edema (DME). Depending on the type of fluid accumulations, different treatments are needed. In particular, Cystoid Macular Edemas (CMEs) represent the most severe scenario, while Diffuse Retinal Thickening (DRT) is an early indicator of the disease but a challenging scenario to detect. While methodologies exist, their explanatory power is limited to the input sample itself. However, due to the complexity of these accumulations, this may not be enough for a clinician to assess the validity of the classification. Thus, in this work, we propose a novel approach based on multi-prototype networks with vision transformers to obtain an example-based explainable classification. Our proposal achieved robust results in two representative OCT devices, with a mean accuracy of 0.9099 ± 0.0083 and 0.8582 ± 0.0126 for CME and DRT-type fluid accumulations, respectively.

COVID-19 is a challenging disease that was declared as global pandemic in March 2020. As the main impact of this disease is located in the pulmonary regions, chest X-ray devices are very useful to understand the severity of the disease on each patient. In order to reduce the risk of cross-contamination, the radiologists are recommended to use portable devices instead of fixed machinery, as these devices are easier to decontaminate. Moreover, the development of reliable and robust methodologies of computer-aided diagnosis systems is very relevant to reduce the workload that expert clinicians are experiencing in the current moment. In this work, we propose a comprehensive analysis of the deep features extracted from portable chest X-ray captures to perform a COVID-19 screening. We also study the optimal characterization of the problem with a lower dimensionality, contrasting the results of the feature selection methods that were chosen. Results demonstrated that the proposed approach is robust and reliable, obtaining a 90.43% of accuracy for the test set, using only 46.85% of the deep features in the context of poor quality and low detail X-ray images obtained from portable devices.

Patients at high risk of contracting COVID-19 require specialized monitoring throughout their illness to ensure optimal treatment at each stage. To support this monitoring, Computer-Aided Diagnosis (CAD) methods analyze clinical data to estimate the most likely outcome for each patient, using various clinical variables such as symptoms, medical history, and laboratory results to predict outcomes. Despite the numerous proposals for COVID-19 diagnosis using CAD methods, the lack of explainability in many machine learning models poses a challenge in incorporating these methods into clinical practice. Additionally, other crucial tasks such as estimating the risk of death or severe forms of the disease must be considered to identify cases that require greater monitoring. To overcome these challenges, we propose an explainable methodology for estimating the risk of hospitalization and death in COVID-19 patients using clinical data. Our methodology employs four machine learning algorithms, three feature selection methods, and a decision tree to provide explainability. Our approach achieves an accuracy of 86.16% ± 0.74% for the estimation of hospitalization risk with 29 features, and an accuracy of 86.40% ± 1.80% for the estimation of the risk of death with 26 features. Moreover, our methodology provides valuable insights into the relationship between clinical variables and patient outcomes, which can inform more robust and informed clinical decision-making and improve our understanding of the disease. We demonstrate the potential of our transparent and effective CAD methods to support clinical decision-making in COVID-19 patient care and further research, offering a promising solution to overcome the challenges in incorporating CAD methods into clinical practice.

Abstract Optical Coherence Tomography Angiography or OCTA represents one of the main means of diagnosis of Age-related Macular Degeneration (AMD), the leading cause of blindness in developed countries. This eye disease is characterized by Macular Neovascularization (MNV), the formation of vessels that tear through the retinal tissues. Four types of MNV can be distinguished, each representing different levels of severity. Both the aggressiveness of the treatment and the recovery of the patient rely on an early detection and correct diagnosis of the stage of the disease. In this work, we propose the first fully-automatic grading methodology that considers all the four clinical types of MNV at the three most relevant OCTA scanning depths for the diagnosis of AMD. We perform both a comprehensive ablation study on the contribution of said depths and an analysis of the attention maps of the network in collaboration with experts of the domain. Our proposal aims to ease the diagnosis burden and decrease the influence of subjectivity on it, offering a explainable grading through the visualization of the attention of the expert models. Our grading proposal achieved satisfactory results with an AUC of 0.9224 ± 0.0381. Additionally, the qualitative analysis performed in collaboration with experts revealed the relevance of the avascular plexus in the grading of all three types of MNV (despite not being directly involved in some of them). Thus, our proposal is not only able to robustly detect MNV in complex scenarios, but also aided to discover previously unconsidered relationships between plexuses.

Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND.

The study of the ciliary muscle represents a fundamental step in the diagnosis and treatment of many high-incidence diseases, such as glaucoma or myopia. Currently, Anterior Segment Optical Coherence Tomography (AS-OCT) is widely used by clinicians to analyse the morphological changes that affect this important ocular structure. AS-OCT is a non-invasive imaging technique that produces high-resolution cross-sectional images, allowing a precise visualization of the main ocular tissues of the anterior segment of the eye. In this work, we propose a novel methodology for the ciliary muscle segmentation using AS-OCT images, an emerging ophthalmic imaging technology with great potential to support early diagnosis of relevant ocular conditions. For this purpose, we have analysed the performance of the U-Net architecture with two different encoders (ResNet-18 and ResNet-34) combined with a transfer learning-based approach. The validation of the proposed system was performed through different and representative experiments, using an AS-OCT dataset that was specifically designed for this work. The results demonstrated that the proposed system is robust and reliable, achieving an average Precision of 0.8902 ± 0.0815, an average Recall of 0.8237 ± 0.1239, an average Accuracy of 0.9961 ± 0.0021, an average Jaccard of 0.7431 ± 0.1116 and an average Dice of 0.8445 ± 0.0870. These results demonstrate that the proposed method has a satisfactory performance that can help the clinicians to make a more accurate diagnosis and proceed with appropriate treatments of different diseases of interest.

Abstract 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 device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.

Tuberculosis is an infectious disease that mainly affects the lung tissues. Therefore, chest X-ray imaging can be very useful to diagnose and to understand the evolution of the pathology. This image modality has a poorer quality in contrast with other techniques as the magnetic resonance or the computerized tomography, but chest X-ray is easier and cheaper to perform. Furthermore, data scarcity is challenging in the domain of biomedical imaging. In order to mitigate this problem, the use of Generative Adversarial Network models for image generation has proved to be a powerful approach to train the deep learning models with small datasets, representing an alternative to classic data augmentation strategies. In this work, we propose a fully automatic approach for the generation of novel synthetic chest X-ray images to mitigate the effect of data scarcity in order to improve the tuberculosis screening performance using 3 different publicly available representative datasets: Montgomery County, Shenzhen and TBX11K. Firstly, this approach trains image translation models with a large-sized dataset (TBX11K). Then, these models are used to generate the novel set of synthetic images using small-sized and medium-sized datasets (Montgomery County and Shenzhen, respectively). Finally, the novel set of generated images is added to the training set to improve the performance of an automatic tuberculosis screening. As a result, we obtained an 88.41% ± 5.27% of accuracy for the Montgomery County dataset and a 90.33% ± 1.41% for the Shenzhen dataset. These results demonstrate that the proposed method outperforms previous state-of-the-art approaches.

Central serous chorioretinopathy is one of the most frequent causes of vision impairment among middle-aged adults. Optical Coherence Tomography (OCT) is a non-invasive diagnostic technique that is commonly used for the monitoring of this relevant eye disease. In this context, this paper proposes a fully automatic system for the characterization of intraretinal pathological fluid regions associated with central serous chori-oretinopathy using OCT scans. To achieve this, we adapted an end-to-end fully convolutional architecture for semantic pixel-wise segmentation. The proposed methodology was tested using a heterogeneous set of 100 OCT scans of different patients. Satisfactory results were obtained, reaching values of 0.9954±0.0007, 0.8792 ±0.0079 and 0.9651 ±0.0041 for the mean Accuracy, mean Jaccard index and mean Dice coefficient, respectively. The proposed system also demonstrated its competitive performance with respect to other state-of-the-art approaches.

Nowadays, Diabetic Macular Edema (DME) is one of the leading causes of blindness in developed countries, and its characterized by the presence of pathological fluid accumulations inside the retinal layers. Currently, the main way to detect these fluid accumulations (as well as their severity) is through the use of Optical Coherence Tomography (OCT) imaging. In particular, this ophthalmological image modality allows a precise non-invasive analysis of the morphology of the retina and its structures. Due to the complexity of attempting to successfully segment these fluid accumulations, an alternative paradigm for their detection has been recently proposed. This paradigm, based on a diffuse representation of the pathological regions, creates an intuitive representation of the pathological regions based on a confidence map. Currently, there are only two approaches for this paradigm: one based on a predefined library of texture and intensity features with established machine learning algorithms and other based on deep learning methods. Both approaches have proven to offer satisfactory results, but each one of them performs better in different scenarios. In this work, we perform a complete analysis and comparison on the behaviour and performance of both strategies in a clinical screening scenario to evaluate the suitability of both approaches for the clinical practice as well as their performance as computer vision strategies.

Computer-aided diagnosis plays an important role in the COVID-19 pandemic. Currently, it is recommended to use X-ray imaging to diagnose and assess the evolution in patients. Particularly, radiologists are asked to use portable acquisition devices to minimize the risk of cross-infection, facilitating an effective separation of suspected patients with other low-risk cases. In this work, we present an automatic COVID-19 screening, considering 6 representative state-of-the-art deep network architectures on a portable chest X-ray dataset that was specifically designed for this proposal. Exhaustive experimentation demonstrates that the models can separate COVID-19 cases from NON-COVID-19 cases, achieving a 97.68% of global accuracy.

Coronavirus Disease 2019 (COVID-19), declared a global pandemic by the World Health Organization, mainly affects the pulmonary tissues, playing chest X-ray images an important role for its screening and early detection. In this context, portable X-ray devices are widely used, representing an alternative to fixed devices in order to reduce risks of cross-contamination. However, they provide lower quality and detailed images in terms of spatial resolution and contrast. In this work, given the low availability of images of this recent disease, we present new approaches to artificially increase the dimensionality of portable chest X-ray datasets for COVID-19 diagnosis. Hence, we combined 3 complementary CycleGAN architectures to perform a simultaneous oversampling using an unsupervised strategy and without the necessity of paired data. Despite the poor quality of the portable X-ray images, we provide an overall accuracy of 92.50% in a COVID-19 screening context, proving their suitability for COVID-19 diagnostic tasks.

The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 (i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.

One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of 0.9761±0.0100 for patients with COVID-19, 0.9801±0.0104 for normal patients and 0.9769±0.0111 for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.

Diabetic Macular Edema (DME) is one of the most common causes of vision impairment and blindness in individuals with diabetes. Among the different imaging modalities, Optical Coherence Tomography (OCT) is a non-invasive ophthalmological imaging technique that is commonly used for the diagnosis, monitoring and treatment of DME. In this context, this paper proposes a new methodology for the automatic classification of DME using OCT images. Firstly, the method extracts a set of deep features from the target OCT images using a transfer learning-based approach. Then, the most relevant subset of deep features is selected using different feature selection strategies. Finally, a machine learning approach is applied to test the potential of the implemented method. The proposed methodology was validated using an OCT image dataset retrieved from 400 different patients, being 200 with DME and 200 normal cases. The proposed system achieved satisfactory results, reaching a best accuracy of 97.50%, using only 14.65% of the deep features in the classification of this ocular pathology, demonstrating also its competitive performance with respect to others approaches of the state-of-the-art.

Optical Coherence Tomography (OCT) is a medical imaging modality that is currently the focus of many advancements in the field of ophthalmology. It is widely used to diagnose relevant diseases like Diabetic Macular Edema (DME) or Age-related Macular Degeneration (AMD), both among the principal causes of blindness. These diseases have in common the presence of pathological cystoid fluid accumulations inside the retinal layers that tear its tissues, hindering the correct vision of the patient. In the last years, several works proposed a variety of methodologies to obtain a precise segmentation of these fluid regions. However, many cystoid patterns present several difficulties that harden significantly the process. In particular, some of these cystoid bodies present diffuse limits, others are deformed by shadows, appear mixed with other tissues and other complex situations. To overcome these drawbacks, a regional analysis has been proven to be reliable in these problematic regions. In this work, we propose the use of the DenseNet architecture to perform this regional analysis instead of the classical machine learning approaches, and use it to represent the pathological identifications with an intuitive color map. We trained, validated and tested the DenseNet neural network with a dataset composed of 3247 samples labeled by an expert. They were extracted from 156 images taken with two of the principal OCT devices of the domain. Then, this network was used to generate the color map representations of the cystoid areas in the OCT images. Our proposal achieved robust results in these regions, with a satisfactory 97.48% ± 0.7611 mean test accuracy as well as a mean AUC of 0.9961 ± 0.0029.

Optical Coherence Tomography Angiography (OCTA) constitutes a novel ophthalmological image modality that is characterized for being a non-invasive capture technique that allows a profound analysis of the vascular characteristics of the eye fundus. Given the restricted field of view of the eye fundus that offers each scan, the specialists frequently capture several complementary images that may be simultaneously analyzed to offer a complete and accurate diagnosis of the patient. In this work, we propose a fully automatic method to register complementary OCTA images and provide compositions for the same patient, generating a wide field of representation that allows a simpler and more direct analysis than the traditional tedious manual procedures. To achieve this, we based our proposal in a robust combination of representative features that are filtered by an accurate identification of the main retinal vasculature. This way, given the characteristic high irregularity in the fundus of the OCTA images, we avoid many variable areas that may interfere in the registration process, restricting the analysis to the most representative and stable structure of this image modality, the main retinal vasculature. In particular, we use Speeded-Up Robust Features (SURF) algorithm to extract representative features in the main vascular region that is extracted using a method that combines the analysis of the Hessian matrix followed by an hysteresis threshold process. Then, using a K-NN model, we perform the registration of the resulting features from the different OCTA images to be analyzed. Finally, the Random sample consensus (RANSAC) method is exploited to produce the final target mosaic. The proposed method presented satisfactory results in the validation experiments, with accurate values for the MSE index of 1.2566 and 1.6725 pixels for the registration of paired images an mosaics, respectively.

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows a complete analysis and evaluation of the main retinal structures and their histopathology properties. Diabetic Macular Edema (DME) implies the accumulation of intraretinal fluid within the macular region. Diffuse Retinal Thickening (DRT) edemas are considered a relevant case of DME disease, where the pathological regions are characterized by a “sponge-like” appearance and a reduced intraretinal reflectivity, being visible in OCT images. Additionally, the presence of other structures may alter the OCT image characteristics, confusing the pathological identification process. This is the case of the retinal vessels over all the eye fundus, whose presence produce shadow projections over the retinal layers that may hide the “sponge-like” appearance of the DRT edemas. Thus, in this paper, we present a proposal for the automatic extraction of DRT edemas, also using as reference the information provided by the automatic identifications of the retinal vessels in the OCT images. To do that, firstly, the system delimits three retinal regions of interest. These retinal regions facilitate the posterior identification of the vessel structures and the segmentation of the DRT regions. For the identification of the vessels structures, the method combined the localization of the upper bright vascular profiles with the presence of their corresponding lower dark vascular shadows. Finally, a learning strategy is implemented for the segmentation of the DRT edemas. Satisfactory results were obtained, reaching values of 0.8346 and 0.9051 of Jaccard index and Dice coefficient, respectively, for the extraction of the existing DRT edemas.

22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018, Belgrade, 3 September 2018 - 5 September 2018

Diabetic retinopathy is one of the leading causes of vision impairment that is commonly associated to the Macular Edema (ME) disease. The Diffuse Retinal Thickening (DRT) is a ME type derived from the local intraretinal fluid accumulation in the lower retinal layers, producing significant morphological alterations in the eye fundus. The presence and properties of these intraretinal fluids are used by the ophthalmologists as significant indicators of the clinical stage of the ME disease. Given that, the precise identification and segmentation of the DRT edema type allow the early diagnosis of the ME disease which, therefore, permits a better adjustment of the treatments, reducing their costs as well as improving the life quality of the patients. This paper proposes a novel methodology for the automatic identification and segmentation of the DRT edemas using Optical Coherence Tomography (OCT) images as source of information. Firstly, the method identifies four of the principal retinal layers that are used as reference to delimit the outer retina, region where the DRT edemas are typically originated. Inside this region, a large and heterogeneous set of features was defined to recognize the characteristic “sponge-like” patterns of the DRT edema, using intensity, texture and clinically-defined features. For this analysis, four representative classifiers were employed with the best subsets of previously selected features. This methodology was tested using 70 OCT images from where 560 samples were extracted with the presence and absence of DRT edemas. The best results were achieved by the 7-kNN classifier, reaching in the detection stage an accuracy of 0.9366, whereas in the segmentation stage obtained values of 0.6625 and 0.7899 for the Jaccard and Dice coefficients, respectively.

Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows the analysis of the eye fundus characteristics in real time. These images enable the experts to make a clinical evaluation of the retinal vasculature, whose morphology provides relevant information for diseases like diabetes, hypertension or arteriosclerosis. In this paper, we present a novel proposal for the automatic vasculature identification in retinal OCT images. To achieve this, we analyse the intensity profiles between representative retinal layers, previously segmented. Then, two statistical models are generated using representative samples of vessel and non-vessel profiles. The analysis of both statistical models let us optimize the discrimination of both cathegories that is used, finally, to identify the vessel locations. The proposed method was adjusted and validated using 256 OCT images, including 1274 vascular structures that were labelled by an expert clinician. Satisfactory results were provided as a precision of 94.55% and a recall of 90.25% were obtained, respectively. The method facilitates the doctors’ work allowing better analysis and treatments of vascular diseases.

Optical Coherence Tomography (OCT) is, nowadays, a clinical standard imaging technique in opthalmology as it provides more information than other classical modalities as can be, for instance, retinographies. OCT scans show a 3D representation of the real layout of the eye fundus in a non-invasive way, letting clinicians inspect deeply the retinal layers in a cross-sectional visualization. For that reason, OCT scans are commonly used in the study of the retinal morphology and the identification of pathological structures. Among them, an appropriate identification and analysis of any present intraretinal cystoid region is crucial to perform an adequate diagnosis of the exudative macular disease, one of the main causes of blindness in developed countries. In this work, we analyzed and characterized the intraretinal cystoid regions in OCT images by the definition of a complete and heterogeneous set of 326 intensity and texture-based features. Relief-F and L0 feature selectors were used in order to identify the optimal feature subsets that provide the best discriminative power. Representative classifiers, as the Linear Bayes Normal Classifier (LDC), Quadratic Bayes Normal Classifier (QDC) and K-Nearest Neighbor Classifier (KNN) were finally used to evaluate the potential of identification of the feature subsets. The method was validated using 51 OCT images. From them, 363 and 360 samples of cystoid and non-cystoid regions were selected, respectively. The best results were offered by the LDC classifier that, using a feature subset identified by the L0 selector, provided an accuracy of 0.9060.