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About

My name is Ana Maria Mendonça and I am currently Associate Professor at the Department of Electrical and Computer Engineering (DEEC) of the Faculty of Engineering of the University of Porto (FEUP), where I got my PhD in 1994. I was a researcher at the Institute for Biomedical Engineering (INEB) until 2014, but since 2015 I am a senior researcher at INESC TEC.

For several years, I was a member of the Executive council of DEEC and at present I am the Vice-Dean of FEUP. At INEB, I was a member of the Board of Directors and afterwards President of the Board. I was an elected member of the pedagogical council of FEUP and currently I am also a member of the scientific council. I was the Director of the Master in Biomedical Engineering from 2009 to 2014 and I am a member of the scientific committee of the Doctoral Programme in Biomedical Engineering.

I have been collaborating as a research and also as responsible in several research projects, mostly dedicated to the development of image analysis and classification methodologies aiming at extracting essential information from medical images in order to support the diagnosis process. Past work has been mostly devoted to three main areas: retinal pathologies, lung diseases and genetic disorders, but ongoing work is mainly focused on the development of Computer-Aided Diagnosis systems in Ophthalmology and Radiology.

Interest
Topics
Details

Details

003
Publications

2019

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

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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

Supervised
thesis

2017

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

Author
Carlos Alexandre Nunes Ferreira

Institution
UP-FEUP

2016

Estimation of choroidal thickness in OCT images

Author
Simão Pedro Marques Pinto de Faria

Institution
UP-FEUP

2016

Estimation of Vessel Caliber in Retinal Images

Author
Teresa Manuel Sá Finisterra Araújo

Institution
UP-FEUP

2016

Detection of red lesions associated with Diabetic Retinopathy

Author
Tânia Filipa Fernandes Melo

Institution
UP-FEUP

2015

Advanced Image Analysis for the Assessment of Retinal Vascular Changes

Author
Behdad Dasht Bozorg

Institution
UP-FEUP