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5 results on '"Alba, Xenia"'

1. High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

Author

Attar, Rahman, Pereanez, Marco, Gooya, Ali, Alba, Xenia, Zhang, Le, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., and Frangi, Alejandro F.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge,this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework., Comment: Accepted in STACOM workshop of MICCAI2018

Published
2019

5. Statistical shape modeling of the left ventricle: myocardial infarct classification challenge

Author

Xingyu Zhang, Xènia Albà, Pau Medrano-Gracia, Allen Lu, Peter Claes, Daniel Rueckert, Pierre Ablin, Sotirios A. Tsaftaris, Xavier Pennec, J. E. Allen, Marc-Michel Rohé, Marco Pereanez, Kaleem Siddiqi, Jan Ehrhardt, Serkan Çimen, Nicolas Duchateau, Avan Suinesiaputra, Vicente Grau, Catarina Pinto, Alejandro F. Frangi, Ilkay Oksuz, Karim Lekadir, Anirban Mukhopadhyay, Mahdi Tabassian, Martino Alessandrini, Alistair A. Young, Luciano Teresi, Nripesh Parajuli, Matthias Wilms, Maxime Sermesant, Brett R. Cowan, Ali Gooya, Jan D'hooge, Wenjia Bai, Paolo Piras, Dennis Säring, Department of Anatomy and Medical Imaging [Auckland], Faculty of Medical and Health Sciences [Auckland], University of Auckland [Auckland]-University of Auckland [Auckland], Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Departament de Tecnologies de la Informació i les Comunicacions [Barcelona] (ETIC / UPF), Universitat Pompeu Fabra [Barcelona] (UPF), Department of Cardiovascular Sciences [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Department of electrical, electronic and information engineering 'GUGLIELMO MARCONI' [Bologna] (DEI), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Department of Engineering Science [Oxford], Institute of Biomedical Engineering [Oxford] (IBME), University of Oxford-University of Oxford, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Center for Computational Imaging and Simulation Technologies in Biomedicine [Sheffield] (CISTIB), University of Sheffield [Sheffield], Department of Electrical Engineering [KU Leuven] (KU-ESAT), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Medical Informatics [Lübeck], Universität zu Lübeck = University of Lübeck [Lübeck], Department of Electrical Engineering [Yale University], Yale University [New Haven], Department of Biomedical Engineering [Yale University], Zuse Institute Berlin (ZIB), IMT Institute for Advanced Studies [Lucca], E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Dipartimento di Ingegneria Strutturale e Geotecnica [Rome], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), University of Applied Sciences Wedel, Departimento di Matematica E-Fisica [Rome], Università degli Studi Roma Tre = Roma Tre University (ROMA TRE), Suinesiaputra, Avan, Ablin, Pierre, Alba, Xenia, Alessandrini, Martino, Allen, Jack, Bai, Wenjia, Cimen, Serkan, Claes, Peter, Cowan, Brett R, D'Hooge, Jan, Duchateau, Nicola, Ehrhardt, Jan, Frangi, Alejandro F, Gooya, Ali, Grau, Vicente, Lekadir, Karim, Lu, Allen, Mukhopadhyay, Anirban, Oksuz, Ilkay, Parajali, Nripesh, Pennec, Xavier, Pereanez, Marco, Pinto, Catarina, Piras, Paolo, Rohe, Marc-Michel, Rueckert, Daniel, Saring, Denni, Sermesant, Maxime, Siddiqi, Kaleem, Tabassian, Mahdi, Teresi, Luciano, Tsaftaris, Sotirios A, Wilms, Matthia, Young, Alistair A, Zhang, Xingyu, Medrano-Gracia, Pau, University of Oxford [Oxford]-University of Oxford [Oxford], Universität zu Lübeck [Lübeck], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], and Università degli Studi Roma Tre

Subjects
Computer science, Feature extraction, statistical shape analysis, computer science applications1707 computer vision and pattern recognition, 030204 cardiovascular system & hematology, computer.software_genre, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Health Information Management, Medical imaging, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Myocardial infarction, Electrical and Electronic Engineering, myocardial infarct, Ventricular remodeling, Set (psychology), Computational model, Receiver operating characteristic, shape modelling, business.industry, Statistical shape analysis, Pattern recognition, Cardiac modeling, classification, biotechnology, electrical and electronic engineering, health information management, medicine.disease, 3. Good health, Computer Science Applications, Artificial intelligence, Data mining, business, computer, Biotechnology
Abstract

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to 1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and 2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website. 1 1 http://www.cardiacatlas.org .

Published
2018

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