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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

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

2019

Lightweight deep learning pipeline for detection, segmentation and classification of breast cancer anomalies

Authors
Oliveira, HS; Teixeira, JF; Oliveira, HP;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation. © Springer Nature Switzerland AG 2019.

2019

Automatic Sternum Segmentation in Thoracic MRI

Authors
Dias, M; Rocha, B; Teixeira, JF; Oliveira, HP;

Publication
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Abstract
The Sternum is a human bone located in the anterior area of the thoracic cage. It is present in most of the axial cuts provided from the Magnetic Resonance Imaging (MRI) acquisitions, used in the medical field. Detecting the Sternum is relevant as it contains rigid key-points for 3D model reconstructions, assisting in the planning and evaluation of several surgical procedures, and for atlas development by segmenting structures in anatomical proximity. In the absence of applicable approaches for this specific problem, this paper focuses on two distinct automated methods for Sternum segmentation in MRI. The first, relies on K-Means (Clustering) to perform the segmentation, while the second encompasses the closed Minimum Path over the elliptical transformation of Gradient images. A dataset of 14 annotated acquisitions was used for evaluation. The results favored the Gradient approach over Clustering. © 2019 IEEE.

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