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About

João Teixeira was born in Porto, Portugal, in 1991. He received his M.Sc. in Electrical and Computers Engineering at the Faculty of Engineering of University of Porto, Portugal, in 2014. In 2014, he started his Ph.D. studies at Faculty of Sciences, University of Porto, Portugal, under the joint program in Informatics (MAP-i). He has been working since 2015 as a researcher at INESC TEC, an R&D institute affiliated to University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI), and the Breast Research Group. João Teixeira has a standing collaboration and has been consulting for a R&D department of the Faculty of Medicine (FMUP), the Center for Health Technology and Services Research (CINTESIS), since 2013.
His main research interests include computer vision, image processing, signal processing, with particular interest in medical applications (Breast cancer and Respiratory conditions) and m-health initiatives.

For further information please consult the CV:
https://www.cienciavitae.pt//pt/6E1E-F57C-A94D 

Interest
Topics
Details

Details

004
Publications

2021

Adversarial Data Augmentation on Breast MRI Segmentation

Authors
Teixeira, JF; Dias, M; Batista, E; Costa, J; Teixeira, LF; Oliveira, HP;

Publication
APPLIED SCIENCES-BASEL

Abstract
The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator's architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

2020

Automatic Quality Assessment of a Forced Expiratory Manoeuvre Acquired with the Tablet Microphone

Authors
Almeida, R; Pinho, B; Jacome, C; Teixeira, JF; Amaral, R; Goncalves, I; Lopes, F; Pinheiro, AC; Jacinto, T; Paixao, C; Pereira, M; Marques, A; Fonseca, JA;

Publication
IFMBE Proceedings

Abstract
Evaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed. © 2020, Springer Nature Switzerland AG.

2020

B-Mode Ultrasound Breast Anatomy Segmentation

Authors
Teixeira, JF; Carreiro, AM; Santos, RM; Oliveira, HP;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences

Authors
Teixeira, JF; Bessa, S; Gouveia, PF; Oliveira, HP;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

Authors
BESSA, S; TEIXEIRA, JF; CARVALHO, PH; GOUVEIA, PF; OLIVEIRA, HP;

Publication
Proceedings of 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 November 2020

Abstract

Supervised
thesis

2017

Automated Detection of Anatomical structure keypoints on medical imaging algorithms directed for X-Ray Mammography

Author
Hugo Manuel Soares Oliveira

Institution
UP-FCUP

2017

Automated Detection of Bone structur keypoints on Magnetic Resonance Imaging - Sternum and Clavicules

Author
Beatriz Gonçalves Rocha

Institution
UP-FEUP

2015

Conceção e desenvolvimento de uma aplicação móvel para monitoramento da tosse em crianças

Author
José Manuel da Silva Fernandes

Institution
UP-FMUP