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Sobre

Sobre

O meu nome é Ana Maria Mendonça e sou Professora Associada do Departamento de Engenharia Eletrotécnica (DEEC) da Faculdade de Engenharia da Universidade do Porto (FEUP). Foi nesta Universidade que concluí o meu doutoramento em 1994. Fui investigadora do Instituto de Engenharia Biomédica (INEB) até 2014, mas a partir de 2015 integrei o Centro de Investigação em Engenharia Biomédica em do INESC TEC como investigadora sénior.

Na minha atividade de gestão de ensino superior e investigação, fui membro do Conselho Executivo do DEEC e sou atualmente Subdiretora da FEUP. No INEB, integrei a Direção do Instituto inicialmente como vogal e posteriormente como Presidente da Direção. Fui membro eleito do Conselho pedagógico da FEUP e sou atualmente membro do Conselho Científico desta escola. Integro a comissão científica do Programa Doutoral em Engenharia Biomédica e de 2009 a 2014 fui Diretora do Mestrado em Engenharia Biomédica da FEUP.

Tenho colaborado como investigadora ou como responsável em diversos projetos de investigação, dominantemente na área da imagem biomédica. O meu trabalho de investigação centrou-se essencialmente no desenvolvimento de metodologias de análise de imagem e classificação tendo como objetivo a extração de informação útil de imagens médicas para apoiar o diagnóstico médico. O trabalho passado foi dedicado principalmente às patologias da retina, do pulmão e doenças genéticas, mas o trabalho atual está essencialmente focado no desenvolvimento de sistema de apoio ao diagnóstico em oftalmologia e radiologia.

Tópicos
de interesse
Detalhes

Detalhes

003
Publicações

2019

An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans

Autores
Shakibapour, E; Cunha, A; Aresta, G; Mendonca, AM; Campilho, A;

Publicação
Expert Systems with Applications

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative – LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value = 92.16% with a significance level of 5%. © 2018 Elsevier Ltd

2019

Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

Autores
Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

Publicação
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2018

Retinal image quality assessment by mean-subtracted contrast-normalized coefficients

Autores
Galdran, A; Araujo, T; Mendonca, AM; Campilho, A;

Publicação
Lecture Notes in Computational Vision and Biomechanics

Abstract
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost. © 2018, Springer International Publishing AG.

2018

3D mapping of choroidal thickness from OCT B-scans

Autores
Faria, SP; Penas, S; Mendonca, L; Silva, JA; Mendonca, AM;

Publicação
Lecture Notes in Computational Vision and Biomechanics

Abstract
The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists. © 2018, Springer International Publishing AG.

2018

End-to-end Adversarial Retinal Image Synthesis

Autores
Costa, P; Galdran, A; Meyer, MI; Niemeijer, M; Abramoff, M; Mendonca, AM; Campilho, A;

Publicação
IEEE Transactions on Medical Imaging

Abstract
In medical image analysis applications, the availability of large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a Generative Adversarial Network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality. IEEE

Teses
supervisionadas

2017

Quantitative assessment of Central Serous Chorioretinopathy in Angiographic sequences of retinal images

Autor
Carlos Alexandre Nunes Ferreira

Instituição
UP-FEUP

2016

Estimation of choroidal thickness in OCT images

Autor
Simão Pedro Marques Pinto de Faria

Instituição
UP-FEUP

2016

Estimation of Vessel Caliber in Retinal Images

Autor
Teresa Manuel Sá Finisterra Araújo

Instituição
UP-FEUP

2016

Detection of red lesions associated with Diabetic Retinopathy

Autor
Tânia Filipa Fernandes Melo

Instituição
UP-FEUP

2015

Advanced Image Analysis for the Assessment of Retinal Vascular Changes

Autor
Behdad Dasht Bozorg

Instituição
UP-FEUP