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About

Aurélio Campilho is Professor in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Portugal. He is a Senior Member of the IEEE – The Institute of Electrical and Electronics Engineers. He is coordinator of the Center for Biomedical Engineering Research (C-BER) and develops research at the Biomedical Imaging Lab from C-BER from INESC TEC – Institute for Systems and Computer Engineering, Technology and Science. His teaching activities are in the  courses: Bioengineering Master Degree: Introduction to Scientific Computing, Biomedical Image Analysis and Computer-aided Diagnosis; Doctoral Degree in Electrical and Computer Engineering: Image Analysis and Recognition. His current research interests include the areas of biomedical engineering, medical image analysis, image processing and computer vision, particularly in Computer-aided Diagnosis applied in several imaging modalities, including ophthalmic images, carotid ultrasound imaging and computed tomography of the lung. He is General Chair of the series of International Conferences on Image Analysis and Recognition (ICIAR).

Interest
Topics
Details

Details

004
Publications

2020

Automatic lung nodule detection combined with gaze information improves radiologists' screening performance

Authors
Aresta, G; Ramos, I; Campilho, A; Ferreira, C; Pedrosa, J; Araujo, T; Rebelo, J; Negrao, E; Morgado, M; Alves, F; Cunha, A;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract

2020

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Authors
Porwal, P; Pachade, S; Kokare, M; Deshmukh, G; Son, J; Bae, W; Liu, LH; Wang, J; Liu, XH; Gao, LX; Wu, TB; Xiao, J; Wang, FY; Yin, BC; Wang, YZ; Danala, G; He, LS; Choi, YH; Lee, YC; Jung, SH; Li, ZY; Sui, XD; Wu, JY; Li, XL; Zhou, T; Toth, J; Bara, A; Kori, A; Chennamsetty, SS; Safwan, M; Alex, V; Lyu, XZ; Cheng, L; Chu, QH; Li, PC; Ji, X; Zhang, SY; Shen, YX; Dai, L; Saha, O; Sathish, R; Melo, T; Araujo, T; Harangi, B; Sheng, B; Fang, RG; Sheet, D; Hajdu, A; Zheng, YJ; Mendonca, AM; Zhang, ST; Campilho, A; Zheng, B; Shen, D; Giancardo, L; Quellec, G; Meriaudeau, F;

Publication
Medical Image Analysis

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

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Authors
Al Hajj, H; Lamard, M; Conze, PH; Roychowdhury, S; Hu, XW; Marsalkaite, G; Zisimopoulos, O; Dedmari, MA; Zhao, FQ; Prellberg, J; Sahu, M; Galdran, A; Araujo, T; Vo, DM; Panda, C; Dahiya, N; Kondo, S; Bian, ZB; Vandat, A; Bialopetravicius, J; Flouty, E; Qiu, CH; Dill, S; Mukhopadhyay, A; Costa, P; Aresta, G; Ramamurthys, S; Lee, SW; Campilho, A; Zachow, S; Xia, SR; Conjeti, S; Stoyanov, D; Armaitis, J; Heng, PA; Macready, WG; Cochener, B; Quellec, G;

Publication
Medical Image Analysis

Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. © 2018 Elsevier B.V.

2019

Wide Residual Network for Lung-Rads™ Screening Referral

Authors
Ferreira, CA; Aresta, G; Cunha, A; Mendonca, AM; Campilho, A;

Publication
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

Abstract

Supervised
thesis

2016

Detection of Juxta-Pleural Lung Nodules in Computed Tomography Images

Author
Guilherme Moreira Aresta

Institution
UP-FEUP

2016

Estimation of Vessel Caliber in Retinal Images

Author
Teresa Manuel Sá Finisterra Araújo

Institution
UP-FEUP

2015

Advanced Image Analysis for the Assessment of Retinal Vascular Changes

Author
Behdad Dasht Bozorg

Institution
UP-FEUP

2015

content based image retrieval as a computer aided diagnosis tool for radiologists

Author
José Ricardo Ferreira de Castro Ramos

Institution
UP-FEUP

2015

Automatic Lung Nodule Classification in Chest Computerized Tomography Images

Author
Luís do Couto Gonçalves

Institution
UP-FEUP