20 research outputs found
Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images
©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). “Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 186(105201), 105201. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2019.105201.[Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images.
Method: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks.
Results: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin.
Conclusions: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions.This work is supported by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project, and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. The authors of this work also receive financial support from the ERDF and European Social Fund (ESF) of the EU, and Xunta de Galicia through Centro Singular de Investigación de Galicia, accreditation 2016–2019, ref. ED431G/01, Grupo de Referencia Competitiva, ref. ED431C 2016-047, and the predoctoral grant contract ref. ED481A-2017/328.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047Xunta de Galicia; ED481A-2017/32
A Multi-task Network to Detect Junctions in Retinal Vasculature
Junctions in the retinal vasculature are key points to be able to extract its
topology, but they vary in appearance, depending on vessel density, width and
branching/crossing angles. The complexity of junction patterns is usually
accompanied by a scarcity of labels, which discourages the usage of very deep
networks for their detection. We propose a multi-task network, generating
labels for vessel interior, centerline, edges and junction patterns, to provide
additional information to facilitate junction detection. After the initial
detection of potential junctions in junction-selective probability maps,
candidate locations are re-examined in centerline probability maps to verify if
they connect at least 3 branches. The experiments on the DRIVE and IOSTAR
showed that our method outperformed a recent study in which a popular deep
network was trained as a classifier to find junctions. Moreover, the proposed
approach is applicable to unseen datasets with the same degree of success,
after training it only once.Comment: MICCAI 2018 Camera Ready Versio
Color Fundus Image Registration Using a Learning-Based Domain-Specific Landmark Detection Methodology
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Medical imaging, and particularly retinal imaging, allows to accurately diagnose many eye pathologies as well as some systemic diseases such as hypertension or diabetes. Registering these images is crucial to correctly compare key structures, not only within patients, but also to contrast data with a model or among a population. Currently, this field is dominated by complex classical methods because the novel deep learning methods cannot compete yet in terms of results and commonly used methods are difficult to adapt to the retinal domain. In this work, we propose a novel method to register color fundus images based on previous works which employed classical approaches to detect domain-specific landmarks. Instead, we propose to use deep learning methods for the detection of these highly-specific domain-related landmarks. Our method uses a neural network to detect the bifurcations and crossovers of the retinal blood vessels, whose arrangement and location are unique to each eye and person. This proposal is the first deep learning feature-based registration method in fundus imaging. These keypoints are matched using a method based on RANSAC (Random Sample Consensus) without the requirement to calculate complex descriptors. Our method was tested using the public FIRE dataset, although the landmark detection network was trained using the DRIVE dataset. Our method provides accurate results, a registration score of 0.657 for the whole FIRE dataset (0.908 for category S, 0.293 for category P and 0.660 for category A). Therefore, our proposal can compete with complex classical methods and beat the deep learning methods in the state of the art.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00 136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095 894-B-I00 research project; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/147 and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). The funding institutions had no involvement in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED481A 2021/147Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0
Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
Medical imaging plays a prominent role in modern clinical practice for numerous
medical specialties. For instance, in ophthalmology, different imaging techniques are
commonly used to visualize and study the eye fundus. In this context, automated
image analysis methods are key towards facilitating the early diagnosis and adequate
treatment of several diseases. Nowadays, deep learning algorithms have already
demonstrated a remarkable performance for different image analysis tasks. However,
these approaches typically require large amounts of annotated data for the training
of deep neural networks. This complicates the adoption of deep learning approaches,
especially in areas where large scale annotated datasets are harder to obtain, such
as in medical imaging.
This thesis aims to explore novel approaches for the automated analysis of medical
images, particularly in ophthalmology. In this regard, the main focus is on
the development of novel deep learning-based approaches that do not require large
amounts of annotated training data and can be applied to high resolution images.
For that purpose, we have presented a novel paradigm that allows to take advantage
of unlabeled complementary image modalities for the training of deep neural
networks. Additionally, we have also developed novel approaches for the detailed
analysis of eye fundus images. In that regard, this thesis explores the analysis of
relevant retinal structures as well as the diagnosis of different retinal diseases. In
general, the developed algorithms provide satisfactory results for the analysis of the
eye fundus, even when limited annotated training data is available.[Resumen]
Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna
de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso
de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este
contexto, los métodos automáticos de análisis de imagen son clave para facilitar
el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la
actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable
rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos
suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de
las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje
profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados
son más difíciles de obtener, como es el caso de la imagen médica.
Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco
principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no
requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan
aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo
paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas
para el entrenamiento de redes neuronales profundas. Además, también
hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del
fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas
relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En
general, los algoritmos desarrollados proporcionan resultados satisfactorios para el
análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos
de entrenamiento etiquetados es limitada.[Resumo]
As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de
numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso
de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste
contexto, os métodos automáticos de análises de imaxe son clave para facilitar o
diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade,
os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento
en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar
grandes cantidades de datos etiquetos para o adestramento das redes neuronais
profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente
en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles
de obter, como é o caso da imaxe médica.
Esta tese ten como obxectivo explorar novos métodos para a análise automática
de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal
é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non
requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse
a imaxes de alta resolución. Para iso, presentamos un novo paradigma que
permite aproveitar modalidades de imaxe complementarias non etiquetadas para o
adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos
métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta
tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de
diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan
resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo
cando a dispoñibilidade de datos de adestramento etiquetados é limitada
Computational Analysis of Fundus Images: Rule-Based and Scale-Space Models
Fundus images are one of the most important imaging examinations in modern ophthalmology
because they are simple, inexpensive and, above all, noninvasive.
Nowadays, the acquisition and
storage of highresolution
fundus images is relatively easy and fast. Therefore, fundus imaging
has become a fundamental investigation in retinal lesion detection, ocular health monitoring and
screening programmes. Given the large volume and clinical complexity associated with these images,
their analysis and interpretation by trained clinicians becomes a timeconsuming
task and is
prone to human error. Therefore, there is a growing interest in developing automated approaches
that are affordable and have high sensitivity and specificity. These automated approaches need to
be robust if they are to be used in the general population to diagnose and track retinal diseases. To
be effective, the automated systems must be able to recognize normal structures and distinguish
them from pathological clinical manifestations.
The main objective of the research leading to this thesis was to develop automated systems capable
of recognizing and segmenting retinal anatomical structures and retinal pathological clinical
manifestations associated with the most common retinal diseases. In particular, these automated
algorithms were developed on the premise of robustness and efficiency to deal with the difficulties
and complexity inherent in these images. Four objectives were considered in the analysis of
fundus images. Segmentation of exudates, localization of the optic disc, detection of the midline
of blood vessels, segmentation of the vascular network and detection of microaneurysms.
In addition, we also evaluated the detection of diabetic retinopathy on fundus images using the
microaneurysm detection method. An overview of the state of the art is presented to compare the
performance of the developed approaches with the main methods described in the literature for
each of the previously described objectives. To facilitate the comparison of methods, the state of
the art has been divided into rulebased
methods and machine learningbased
methods.
In the research reported in this paper, rulebased
methods based on image processing methods
were preferred over machine learningbased
methods. In particular, scalespace
methods proved
to be effective in achieving the set goals.
Two different approaches to exudate segmentation were developed. The first approach is based on
scalespace
curvature in combination with the local maximum of a scalespace
blob detector and
dynamic thresholds. The second approach is based on the analysis of the distribution function of
the maximum values of the noise map in combination with morphological operators and adaptive
thresholds. Both approaches perform a correct segmentation of the exudates and cope well with
the uneven illumination and contrast variations in the fundus images.
Optic disc localization was achieved using a new technique called cumulative sum fields, which was
combined with a vascular enhancement method. The algorithm proved to be reliable and efficient,
especially for pathological images. The robustness of the method was tested on 8 datasets.
The detection of the midline of the blood vessels was achieved using a modified corner detector
in combination with binary philtres and dynamic thresholding. Segmentation of the vascular network
was achieved using a new scalespace
blood vessels enhancement method. The developed
methods have proven effective in detecting the midline of blood vessels and segmenting vascular
networks.
The microaneurysm detection method relies on a scalespace
microaneurysm detection and labelling
system. A new approach based on the neighbourhood of the microaneurysms was used
for labelling. Microaneurysm detection enabled the assessment of diabetic retinopathy detection.
The microaneurysm detection method proved to be competitive with other methods, especially with highresolution
images. Diabetic retinopathy detection with the developed microaneurysm
detection method showed similar performance to other methods and human experts.
The results of this work show that it is possible to develop reliable and robust scalespace
methods
that can detect various anatomical structures and pathological features of the retina. Furthermore,
the results obtained in this work show that although recent research has focused on machine learning
methods, scalespace
methods can achieve very competitive results and typically have greater
independence from image acquisition. The methods developed in this work may also be relevant
for the future definition of new descriptors and features that can significantly improve the results
of automated methods.As imagens do fundo do olho são hoje um dos principais exames imagiológicos da oftalmologia
moderna, pela sua simplicidade, baixo custo e acima de tudo pelo seu carácter nãoinvasivo.
A
aquisição e armazenamento de imagens do fundo do olho com alta resolução é também relativamente
simples e rápida. Desta forma, as imagens do fundo do olho são um exame fundamental
na identificação de alterações retinianas, monitorização da saúde ocular, e em programas de rastreio.
Considerando o elevado volume e complexidade clínica associada a estas imagens, a análise
e interpretação das mesmas por clínicos treinados tornase
uma tarefa morosa e propensa a erros
humanos. Assim, há um interesse crescente no desenvolvimento de abordagens automatizadas,
acessíveis em custo, e com uma alta sensibilidade e especificidade. Estas devem ser robustas para
serem aplicadas à população em geral no diagnóstico e seguimento de doenças retinianas. Para
serem eficazes, os sistemas de análise têm que conseguir detetar e distinguir estruturas normais
de sinais patológicos.
O objetivo principal da investigação que levou a esta tese de doutoramento é o desenvolvimento
de sistemas automáticos capazes de detetar e segmentar as estruturas anatómicas da retina, e os
sinais patológicos retinianos associados às doenças retinianas mais comuns. Em particular, estes
algoritmos automatizados foram desenvolvidos segundo as premissas de robustez e eficácia para
lidar com as dificuldades e complexidades inerentes a estas imagens.
Foram considerados quatro objetivos de análise de imagens do fundo do olho. São estes, a segmentação
de exsudados, a localização do disco ótico, a deteção da linha central venosa dos vasos
sanguíneos e segmentação da rede vascular, e a deteção de microaneurismas. De acrescentar que
usando o método de deteção de microaneurismas, avaliouse
também a capacidade de deteção da
retinopatia diabética em imagens do fundo do olho.
Para comparar o desempenho das metodologias desenvolvidas neste trabalho, foi realizado um
levantamento do estado da arte, onde foram considerados os métodos mais relevantes descritos na
literatura para cada um dos objetivos descritos anteriormente. Para facilitar a comparação entre
métodos, o estado da arte foi dividido em metodologias de processamento de imagem e baseadas
em aprendizagem máquina.
Optouse
no trabalho de investigação desenvolvido pela utilização de metodologias de análise espacial
de imagem em detrimento de metodologias baseadas em aprendizagem máquina. Em particular,
as metodologias baseadas no espaço de escalas mostraram ser efetivas na obtenção dos
objetivos estabelecidos.
Para a segmentação de exsudados foram usadas duas abordagens distintas. A primeira abordagem
baseiase
na curvatura em espaço de escalas em conjunto com a resposta máxima local de um detetor
de manchas em espaço de escalas e limiares dinâmicos. A segunda abordagem baseiase
na
análise do mapa de distribuição de ruído em conjunto com operadores morfológicos e limiares
adaptativos. Ambas as abordagens fazem uma segmentação dos exsudados de elevada precisão,
além de lidarem eficazmente com a iluminação nãouniforme
e a variação de contraste presente
nas imagens do fundo do olho. A localização do disco ótico foi conseguida com uma nova técnica
designada por campos de soma acumulativos, combinada com métodos de melhoramento da rede
vascular. O algoritmo revela ser fiável e eficiente, particularmente em imagens patológicas. A robustez
do método foi verificada pela sua avaliação em oito bases de dados. A deteção da linha central
dos vasos sanguíneos foi obtida através de um detetor de cantos modificado em conjunto com
filtros binários e limiares dinâmicos. A segmentação da rede vascular foi conseguida com um novo
método de melhoramento de vasos sanguíneos em espaço de escalas. Os métodos desenvolvidos mostraram ser eficazes na deteção da linha central dos vasos sanguíneos e na segmentação da rede
vascular. Finalmente, o método para a deteção de microaneurismas assenta num formalismo de
espaço de escalas na deteção e na rotulagem dos microaneurismas. Para a rotulagem foi utilizada
uma nova abordagem da vizinhança dos candidatos a microaneurismas. A deteção de microaneurismas
permitiu avaliar também a deteção da retinopatia diabética. O método para a deteção
de microaneurismas mostrou ser competitivo quando comparado com outros métodos, em particular
em imagens de alta resolução. A deteção da retinopatia diabética exibiu um desempenho
semelhante a outros métodos e a especialistas humanos.
Os trabalhos descritos nesta tese mostram ser possível desenvolver uma abordagem fiável e robusta
em espaço de escalas capaz de detetar diferentes estruturas anatómicas e sinais patológicos
da retina.
Além disso, os resultados obtidos mostram que apesar de a pesquisa mais recente concentrarse
em metodologias de aprendizagem máquina, as metodologias de análise espacial apresentam
resultados muito competitivos e tipicamente independentes do equipamento de aquisição das imagens.
As metodologias desenvolvidas nesta tese podem ser importantes na definição de novos
descritores e características, que podem melhorar significativamente o resultado de métodos automatizados
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Automated retinal analysis
Diabetes is a chronic disease affecting over 2% of the population in the UK [1]. Long-term complications of diabetes can affect many different systems of the body including the retina of the eye. In the retina, diabetes can lead to a disease called diabetic retinopathy, one of the leading causes of blindness in the working population of industrialised countries. The risk of visual loss from diabetic retinopathy can be reduced if treatment is given at the onset of sight-threatening retinopathy. To detect early indicators of the disease, the UK National Screening Committee have recommended that diabetic patients should receive annual screening by digital colour fundal photography [2]. Manually grading retinal images is a subjective and costly process requiring highly skilled staff. This thesis describes an automated diagnostic system based oil image processing and neural network techniques, which analyses digital fundus images so that early signs of sight threatening retinopathy can be identified. Within retinal analysis this research has concentrated on the development of four algorithms: optic nerve head segmentation, lesion segmentation, image quality assessment and vessel width measurements. This research amalgamated these four algorithms with two existing techniques to form an integrated diagnostic system. The diagnostic system when used as a 'pre-filtering' tool successfully reduced the number of images requiring human grading by 74.3%: this was achieved by identifying and excluding images without sight threatening maculopathy from manual screening