UOLO - automatic object detection and segmentation in biomedical images

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0

Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminator. The generator learns to map information from the observing input (i.e., retinal fundus color image), to the output (i.e., binary mask). Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition.Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The proposed model outperformed state-of-the-art-methods by achieving around 0.96% and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image segmentation is performed in less than a second on recent GPU.Comment: 8 pages, Submitted to 21st International Conference of the Catalan Association for Artificial Intelligence (CCIA 2018

Feature Extraction for Retina Image Based on Difference Approaches

Automatic disease diagnosis using biometric images is a difficult job due to image distortion, such as the presence of artifacts, less or excessive light, narrow vessel visibility and differences in inter-camera variability that affect the performance of an approaches. Almost all extraction methods in the blood vessels in the retina produce the main part of the vessel with no patalogical environment. However, an important problem for this method is that extraction errors occur if they are too focused on the thin vessels, the wide vessels will be more detectable and also artificial vessels that may appear a lot. In addition, when focusing on a wide vessel, the extraction of thin vessels tends to disappear and is low. Based on our research, different treatments are needed to extract thin vessels and wide vessels both visually and in contrast. This study aims to adopt feature extraction strategies with different techniques. The method proposed in segmentation and extraction with three different approaches, namely the pattern of shape, color, and texture. Testing segmentation and feature extraction using STARE datasets with five classes of diseases namely Choroidal Neovascularization, Branch Retinal Vein Occlusion, Histoplasmosis, Myelinated Nerve Fibers, and Coats. Image enhancement on Myelinated Nerve disease Fiber is the best result from the image of other diseases with the highest value of PSNR of 35.4933 dB and the lowest MSE of 0.0003 means that the technique is able to repair objects well. The main significance of this work is to increase the quality of segmentation results by applying the Otsu method. Testing of segmentation results shows improvements with the proposed method compared to other methods. Furthermore, the application of different feature extraction methods will get information on disease classification features based on patterns of suitable shapes, colors, and textures

A method for quantifying sectoral optic disc pallor in fundus photographs and its association with peripapillary RNFL thickness

Purpose: To develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fibre layer (pRNFL) thickness. Methods: We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants. Separately, we used images diagnosed by clinical inspection as pale (N=45) and assessed how measurements compared to healthy controls (N=46). We also developed automatic rejection thresholds, and tested the software for robustness to camera type, image format, and resolution. Results: We developed software that automatically quantified disc pallor across several zones in fundus photographs. Pallor was associated with pRNFL thickness globally (\b{eta} = -9.81 (SE = 3.16), p < 0.05), in the temporal inferior zone (\b{eta} = -29.78 (SE = 8.32), p < 0.01), with the nasal/temporal ratio (\b{eta} = 0.88 (SE = 0.34), p < 0.05), and in the whole disc (\b{eta} = -8.22 (SE = 2.92), p < 0.05). Furthermore, pallor was significantly higher in the patient group. Lastly, we demonstrate the analysis to be robust to camera type, image format, and resolution. Conclusions: We developed software that automatically locates and quantifies disc pallor in fundus photographs and found associations between pallor measurements and pRNFL thickness. Translational relevance: We think our method will be useful for the identification, monitoring and progression of diseases characterized by disc pallor/optic atrophy, including glaucoma, compression, and potentially in neurodegenerative disorders.Comment: 44 pages, 20 figures, 7 tables, submitte

Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc

Detecting the locations of the optic disc and fovea is a crucial task towards developing automatic diagnosis and screening tools for retinal disease. We propose to address this challenging problem by investigating the potential of applying deep learning techniques to this field. In the proposed method, simultaneous detection of the centers of the fovea and the optic disc (OD) from color fundus images is considered as a regression problem. A deep multiscale sequential convolutional neural network (CNN) is designed and trained. The publically available MESSIDOR and Kaggle datasets are used to train the network and evaluate its performance. The centers of the fovea and the OD in each image were marked by expert graders as the ground truth. The proposed method achieves an accuracy of 97%, 96.7% for the detection of the OD center and 96.6%, 95.6% for the detection of the foveal center of the MESSIDOR and Kaggle test sets respectively. Our promising results demonstrate the excellent performance of the proposed CNNs in simultaneously detecting the centers of both the fovea and OD without human intervention or handcrafted features. Moreover, we can localize the landmarks of an image in 0.007s. This approach could be used as a crucial part of automated diagnosis systems for better management of eye disease

Análisis de retinografías para la ayuda al diagnóstico de la Degeneración Macular Asociada a la Edad (DMAE)

La Degeneración Macular Asociada a la edad (DMAE) representa el 8.7% de ceguera en todo el mundo, siendo la causa más común en los países desarrollados en individuos mayores de 60 años. De acuerdo con el informe global sobre la salud ocular, publicado por la Organización Mundial de la Salud en el año 2019, se estima que la prevalencia de la DMAE experimentará un aumento del 20% entre 2020 y 2030. El envejecimiento sistemático y acelerado de la población, junto con la estrecha relación existente entre la edad y la DMAE, augura que estas cifras seguirán creciendo con el paso del tiempo. Con el objetivo de evitar las complicaciones más graves de la DMAE, es sumamente importante abordarla en sus primeras etapas. Para este propósito, los exámenes oftalmológicos de screening constituyen una herramienta esencial. En estos exámenes se utilizan diversas técnicas para capturar imágenes de la retina de los pacientes, siendo la retinografía una de las más baratas y accesibles. Posteriormente, dichas imágenes son revisadas por parte de los especialistas en busca de signos de la enfermedad. Debido a su creciente incidencia, el número de imágenes que deberá revisarse será cada vez más elevado. Esto, unido a la escasez de oftalmólogos, hace que sea cada vez más complicado obtener un diagnóstico rápido y preciso de la enfermedad. En este trabajo se propone un método automático basado en Machine Learning para la detección de la DMAE mediante el análisis de retinografías. Para ello se empleó la base de datos pública Automatic Detection Challenge on Age-related Macular degeneration (ADAM), que contiene 400 retinografías de pacientes sanos y con DMAE. Dicha base de datos se dividió en un conjunto de entrenamiento con 300 imágenes y un conjunto de test con 100 imágenes. El método desarrollado se dividió en diferentes etapas. En primer lugar, se llevó a cabo una etapa de preprocesado para mejorar el contraste de las imágenes mediante la aplicación del algoritmo Contrast-Limited Histogram Equalization (CLAHE). A continuación, se seleccionó una región de interés para enfocar las imágenes y eliminar elementos innecesarios como el fondo. Posteriormente, se llevó a cabo una fase de extracción de características de las imágenes. En ella, se emplearon características basadas en color, estadísticos texturales e histogramas de gradiente orientados. De entre todas las características extraídas, se seleccionaron aquellas más relevantes para la clasificación de la presencia o ausencia de DMAE empleando el algoritmo Fast Correlation Based Filter (FCBF). Finalmente, se llevó a cabo una etapa de clasificación en la que se entrenó un clasificador basado en Ensemble Methods formado por arboles de decisión. En cuanto a los resultados sobre el conjunto de test, se alcanzó un 55.1% de sensibilidad, 91.5% de especificidad, 91.2% de precisión, 72.7% de valor predictivo positivo (VPP), 83.3% de valor predictivo negativo (VPN) y 62.5% en la métrica F1. Además, se obtuvo la curva ROC y se calculó el área bajo la curva (Area Under ROC Curve, AUC), que alcanzó un valor de 0.86. Finalmente se calculó la matriz de confusión, con el fin de dar una visión más detallada de los resultados del método propuesto. Los resultados obtenidos indican que es posible llevar a cabo la detección de la DMAE de forma precisa mediante el análisis automático de retinografías. El método propuesto, por tanto, permitiría acortar el tiempo de diagnóstico, reducir la carga de trabajo de los expertos y, como consecuencia, disminuir los costes económicos asociados al tratamiento de la enfermedad.Grado en Ingeniería Biomédic

Aspectos do rastreamento do glaucoma auxiliados por técnicas automatizadas em imagens com menor qualidade do disco óptico

O glaucoma é uma neuropatia óptica cuja progressão pode levar a cegueira. Representa a principal causa de perda visual de caráter irreversível em todo o mundo para homens e mulheres. A detecção precoce através de programas de rastreamento feita por especialistas é baseada nas características do nervo óptico, em biomarcadores oftalmológicos (destacando-se a pressão ocular) e exames subsidiários, com destaque ao campo visual e OCT. Após o reconhecimento dos casos é feito o tratamento com finalidade de estacionar a progressão da doença e melhorar a qualidade de vida dos pacientes. Contudo, estes programas têm limitações, principalmente em locais mais distantes dos grandes centros de tratamento especializado, insuficiência de equipamentos básicos e pessoal especializado para oferecer o rastreamento a toda a população, faltam meios para locomoção a estes centros, desinformação e desconhecimento da doença, além de características de progressão assintomática da doença. Esta tese aborda soluções inovadoras que podem contribuir para a automação do rastreamento do glaucoma utilizando aparelhos portáteis e mais baratos, considerando as necessidades reais dos clínicos durante o rastreamento. Para isso foram realizadas revisões sistemáticas sobre os métodos e equipamentos para apoio à triagem automática do glaucoma e os métodos de aprendizado profundo para a segmentação e classificação aplicáveis. Também foi feito um levantamento de questões médicas relativas à triagem do glaucoma e associá-las ao campo da inteligência artificial, para dar mais sentido as metodologias automatizadas. Além disso, foi criado um banco de dados privado, com vídeos e imagens de retina adquiridos por um smartphone acoplado a lente de baixo custo para o rastreamento do glaucoma e avaliado com métodos do estado da arte. Foram avaliados e analisados métodos de detecção automática de glaucoma utilizando métodos de aprendizado profundo de segmentação do disco e do copo óptico em banco de dados públicos de imagens de retina. Finalmente, foram avaliadas técnicas de mosaico e de detecção da cabeça do nervo óptico em imagens de baixa qualidade obtidas para pré-processamento de imagens adquiridas por smartphones acoplados a lente de baixo custo.Glaucoma is an optic neuropathy whose progression can lead to blindness. It represents the leading cause of irreversible visual loss worldwide for men and women. Early detection through screening programs carried out by specialists is based on the characteristics of the optic papilla, ophthalmic biomarkers (especially eye pressure), and subsidiary exams, emphasizing the visual field and optical coherence tomography (OCT). After recognizing the cases, the treatment is carried out to stop the progression of the disease and improve the quality of patients’ life. However, these screening programs have limitations, particularly in places further away from the sizeable, specialized treatment centers, due to the lack of essential equipment and technical personnel to offer screening to the entire population, due to the lack of means of transport to these centers, due to lack of information and lack of knowledge about the disease, considering the characteristics of asymptomatic progression of the disease. This thesis aims to develop innovative approaches to contribute to the automation of glaucoma screening using portable and cheaper devices, considering the real needs of clinicians during screening. For this, systematic reviews were carried out on the methods and equipment to support automatic glaucoma screening, and the applicable deep learning methods for segmentation and classification. A survey of medical issues related to glaucoma screening was carried out and associated with the field of artificial intelligence to make automated methodologies more effective. In addition, a private dataset was created, with videos and retina images acquired using a low-cost lens-coupled cell phone, for glaucoma screening and evaluated with state-of-the-art methods. Methods of automatic detection of glaucoma using deep learning methods of segmentation of the disc and optic cup were evaluated and analyzed in a public database of retinal images. In the case of deep learning classification methods, these were evaluated in public databases of retina images and in a private database with low-cost images. Finally, mosaic and object detection techniques were evaluated in low-quality images obtained for pre-processing images acquired by cell phones coupled with low-cost lenses