Morphologic Criteria of Lesion Activity in Neovascular Age-Related Macular Degeneration: A Consensus Article

Intravitreal antivascular endothelial growth factor drugs represent the current standard of care for neovascular age-related macular degeneration (nAMD). Individualized treatment regimens aim at obtaining the same visual benefits of monthly injections with a reduced number of injections and follow-up visits, and, consequently, of treatment burden. The target of these strategies is to timely recognize lesion recurrence, even before visual deterioration. Early detection of lesion activity is critical to ensure that clinical outcomes are not compromised by inappropriate delays in treatment, but questions remain on how to effectively monitor the choroidal neovascularization (CNV) activity. To assess the persistence/recurrence of lesion activity in patients undergoing treatment for nAMD, an expert panel developed a decision algorithm based on the morphological features of CNV. After evaluating all current retinal imaging techniques, the panel identified optical coherent tomography as the most reliable tool to ascertain lesion activity when funduscopy is not obvious

Bright lesion detection in retinal images

Master'sMASTER OF SCIENC

Abnormality Detection in Retinal Images

The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians. In this paper, we focus on the implementation of Multiple-Instance Learning (MIL) in the area of medical image mining, particularly to hard exudates detection in retinal images from diabetic patients. Our proposed approach deals with the highly noisy images that are common in the medical area, improving the detection specificity while keeping the sensitivity as high as possible. We have also investigated the effect of feature selection on system performance. We describe how we implement the idea of MIL on the problem of retinal image mining, discuss the issues that are characteristic of retinal images as well as issues common to other medical image mining problems, and report the results of initial experiments.Singapore-MIT Alliance (SMA

Técnicas de análise de imagens para detecção de retinopatia diabética

Orientadores: Anderson de Rezende Rocha. Jacques WainerTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Retinopatia Diabética (RD) é uma complicação a longo prazo do diabetes e a principal causa de cegueira da população ativa. Consultas regulares são necessárias para diagnosticar a retinopatia em um estágio inicial, permitindo um tratamento com o melhor prognóstico capaz de retardar ou até mesmo impedir a cegueira. Alavancados pela evolução da prevalência do diabetes e pelo maior risco que os diabéticos têm de desenvolver doenças nos olhos, diversos trabalhos com abordagens bem estabelecidas e promissoras vêm sendo desenvolvidos para triagem automática de retinopatia. Entretanto, a maior parte dos trabalhos está focada na detecção de lesões utilizando características visuais particulares de cada tipo de lesão. Além do mais, soluções artesanais para avaliação de necessidade de consulta e de identificação de estágios da retinopatia ainda dependem bastante das lesões, cujo repetitivo procedimento de detecção é complexo e inconveniente, mesmo se um esquema unificado for adotado. O estado da arte para avaliação automatizada de necessidade de consulta é composto por abordagens que propõem uma representação altamente abstrata obtida inteiramente por meio dos dados. Usualmente, estas abordagens recebem uma imagem e produzem uma resposta ¿ que pode ser resultante de um único modelo ou de uma combinação ¿ e não são facilmente explicáveis. Este trabalho objetivou melhorar a detecção de lesões e reforçar decisões relacionadas à necessidade de consulta, fazendo uso de avançadas representações de imagens em duas etapas. Nós também almejamos compor um modelo sofisticado e direcionado pelos dados para triagem de retinopatia, bem como incorporar aprendizado supervisionado de características com representação orientada por mapa de calor, resultando em uma abordagem robusta e ainda responsável para triagem automatizada. Finalmente, tivemos como objetivo a integração das soluções em dispositivos portáteis de captura de imagens de retina. Para detecção de lesões, propusemos abordagens de caracterização de imagens que possibilitem uma detecção eficaz de diferentes tipos de lesões. Nossos principais avanços estão centrados na modelagem de uma nova técnica de codificação para imagens de retina, bem como na preservação de informações no processo de pooling ou agregação das características obtidas. Decidir automaticamente pela necessidade de encaminhamento do paciente a um especialista é uma investigação ainda mais difícil e muito debatida. Nós criamos um método mais simples e robusto para decisões de necessidade de consulta, e que não depende da detecção de lesões. Também propusemos um modelo direcionado pelos dados que melhora significativamente o desempenho na tarefa de triagem da RD. O modelo produz uma resposta confiável com base em respostas (locais e globais), bem como um mapa de ativação que permite uma compreensão de importância de cada pixel para a decisão. Exploramos a metodologia de explicabilidade para criar um descritor local codificado em uma rica representação em nível médio. Os modelos direcionados pelos dados são o estado da arte para triagem de retinopatia diabética. Entretanto, mapas de ativação são essenciais para interpretar o aprendizado em termos de importância de cada pixel e para reforçar pequenas características discriminativas que têm potencial de melhorar o diagnósticoAbstract: Diabetic Retinopathy (DR) is a long-term complication of diabetes and the leading cause of blindness among working-age adults. A regular eye examination is necessary to diagnose DR at an early stage, when it can be treated with the best prognosis and the visual loss delayed or deferred. Leveraged by the continuous expansion of diabetics and by the increased risk that those people have to develop eye diseases, several works with well-established and promising approaches have been proposed for automatic screening. Therefore, most existing art focuses on lesion detection using visual characteristics specific to each type of lesion. Additionally, handcrafted solutions for referable diabetic retinopathy detection and DR stages identification still depend too much on the lesions, whose repetitive detection is complex and cumbersome to implement, even when adopting a unified detection scheme. Current art for automated referral assessment resides on highly abstract data-driven approaches. Usually, those approaches receive an image and spit the response out ¿ that might be resulting from only one model or ensembles ¿ and are not easily explainable. Hence, this work aims at enhancing lesion detection and reinforcing referral decisions with advanced handcrafted two-tiered image representations. We also intended to compose sophisticated data-driven models for referable DR detection and incorporate supervised learning of features with saliency-oriented mid-level image representations to come up with a robust yet accountable automated screening approach. Ultimately, we aimed at integrating our software solutions with simple retinal imaging devices. In the lesion detection task, we proposed advanced handcrafted image characterization approaches to detecting effectively different lesions. Our leading advances are centered on designing a novel coding technique for retinal images and preserving information in the pooling process. Automatically deciding on whether or not the patient should be referred to the ophthalmic specialist is a more difficult, and still hotly debated research aim. We designed a simple and robust method for referral decisions that does not rely upon lesion detection stages. We also proposed a novel and effective data-driven model that significantly improves the performance for DR screening. Our accountable data-driven model produces a reliable (local- and global-) response along with a heatmap/saliency map that enables pixel-based importance comprehension. We explored this methodology to create a local descriptor that is encoded into a rich mid-level representation. Data-driven methods are the state of the art for diabetic retinopathy screening. However, saliency maps are essential not only to interpret the learning in terms of pixel importance but also to reinforce small discriminative characteristics that have the potential to enhance the diagnosticDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoCAPE

Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases. However, digital colour fundus images suffer from low and varied contrast, and are also affected by noise, requiring the use of fundus angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to 6 time’s higher contrast. However, FFA is an invasive method that requires contrast agents to be injected and this can lead other physiological problems. A reported digital image enhancement technique named RETICA that combines Retinex and ICA (Independent Component Analysis) techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images