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Biomedical Engineering Research

At C-BER our main goals are the creation of interdisciplinary knowledge enabling innovation and technology transfer with economic impact; and also the development of products, tools and methods for the prevention and early detection of different types of diseases, aging-related impairments, or for human rehabilitation, physical therapy or functional assessment.

We also seek to contribute to the development of advanced neuro-technologies at the frontier of engineering and neurology, and to promote strategic partnerships with clinical partners, research institutes, and fostering international cooperation.

Our R&D activity is developed in three different areas: BioInstrumentation, Biomedical Imaging and NeuroEngineering.

Latest News
Networked Intelligent Systems

Fraunhofer award was given to a paper on retina disease

The paper entitled “Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images”, written by Carlos Ferreira, Jorge Silva and Ana Maria Mendonça, collaborators of INESC TEC’s Centre for Biomedical Engineering Research, alongside Susana Penas, Ophthalmology Specialist at São João Hospital Centre and Professor at FMUP, won the Best Paper Award in the category entitled Full Papers Summarizing a Msc Thesis from the Associação Fraunhofer Portugal Research (Fraunhofer Portugal).

07th March 2019

Networked Intelligent Systems

Original partnership brings together INESC TEC's technology and photographic art

On 23 February, the inauguration of the photography and video exhibition entitled “Olha e Vê. Sente e Vive” and “Outros Retratos e Auto-retratos” (in English “Look and See. Sense and Live” and “Other Portraits and Self-Portraits”) took place at Mira Forum. It was promoted under the project SCREEN-DR of INESC TEC’s Centre for Biomedical Engineering Research (C-BER).

01st March 2019

Networked Intelligent Systems

InSignals Neurotech is the name of the new INESC TEC’s spin-off

Spin-off is the result of the research conducted in the biomedical engineering area.

28th February 2019

Networked Intelligent Systems

INESC TEC organises photographic exhibition that combines art and technology

“Olha e Vê. Sente e Vive” and “Outros Retratos e Auto-retratos” (in English “Look and See. Sense and Live” and “Other Portraits and Self-Portraits”) are the captions that map an initiative organised under the project SCREEN-DR, a computational platform for the diabetic retinopathy screening, coordinated by INESC TEC’s Centre for Biomedical Engineering Research (C-BER).

30th January 2019

Networked Intelligent Systems

Article on diabetic retinopathy wins Best Paper Award in the USA

The article "MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis", in which three INESC TEC’s researchers are co-authors, namely Pedro Costa and Adrian Galdran, collaborators of the Centre for Biomedical Engineering Research (C-BER) on the date of the paper, and Aurélio Campilho, current coordinator of C-BER and Full Professor at the Faculty of Engineering of the University of Porto, received a “best paper award” at ICMLA 2018 - 17th Conference on Machine Learning and Application.

28th January 2019

Interest Topics
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Featured Projects

LUCAS

Lung cancer screening - A non-invasive methodology for early diagnosis

2018-2021

PERFECT

Perceptual equivalence in virtual reality for authentic training

2018-2020

TexBoost

Less Commodities more Specialities

2017-2020

Team
003

Laboratories

Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab

Publications

C-BER Publications

View all Publications

2019

Combined phase and magnitude metric for validation of lower limb multibody dynamics muscle action with sEMG

Authors
Rodrigues, C; Correia, M; Abrantes, J; Nadal, J; Benedetti, M;

Publication
IFMBE Proceedings

Abstract
This study presents and applies combined phase and magnitude metrics for validation of multibody dynamics (MBD) estimated muscle actions with simultaneous registered sEMG of lower limb muscles. Subject-specific tests were performed for acquisition of ground reaction forces and kinematic data from joint reflective markers during NG, SKG and SR. Inverse kinematics and dynamics was performed using AnyBody musculoskeletal personalized modeling and simulation. MBD estimated muscle activity (MA) of soleus medialis (SM) and tibialis anterior (TA) were compared on phase, magnitude and combined metric with simultaneous acquisition of sEMG for the same muscles. Results from quantitative metrics presented better agreement between MDB MA and sEMG on phase (P) than on magnitude (M) with combined (C) metric following the same pattern as the magnitude. Soleus medialis presented for specific subject lower P and M error on NG and SKG than at SR with similar P errors for tibialis anterior and higher error on M for TA at NG and SKG than SR. Separately and combined quantitative metrics of phase and magnitude presents as a suitable tool for comparing measured sEMG and MBD estimated muscle activities, contributing to overcome qualitative and subjective comparisons, need for intensive observer supervision, low reproducibility and time consuming. © Springer Nature Singapore Pte Ltd. 2019.

2019

Spherical angular analysis for pelvis coordination assessment on modified gait

Authors
Rodrigues, C; Correia, M; Abrantes, J; Nadal, J; Benedetti, M;

Publication
IFMBE Proceedings

Abstract
This study presents and applies 3D spherical angular analysis in relation with 2D polar coordinates to assess anatomic pelvic movement on modified gait, namely stiff knee (SKG) gait and slow running (SR) comparing with normal gait (NG). Subject specific analysis was performed of an adult healthy male based on inverse kinematics from in vivo and noninvasive capture at human movement lab of reflective markers position from pelvis anatomical selected points with Qualisys camera system during a complete stride of NG, SKG and SR. Radial distance (R), pitch (?) and azimuth (?) angular phases were computed from pelvic angle-angle diagrams (?T, ?C, ?S) at transverse (T), coronal (C) and sagittal (S) planes, and angular phase (?) and planar radial distance (r) polar coordinates computed from pelvic angle-angle diagrams projections at cartesian planes (?T, ?C), (?T, ?S), (?C, ?S). Average radial distances and phase standard deviation were assessed on spherical and polar coordinates. © Springer Nature Singapore Pte Ltd. 2019.

2019

Methodological considerations for kinematic analysis of upper limbs in healthy and poststroke adults. Part I: A systematic review of sampling and motor tasks

Authors
Mesquita, IA; Pinheiro, ARV; Velhote Correia, MFP; Silva, CICD;

Publication
Topics in Stroke Rehabilitation

Abstract

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

Facts & Figures

15Papers in indexed journals

2016

1Book Chapters

2016

20R&D Services and Consulting (k€)

2016

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