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22 results on '"Albà, Xènia"'

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

4. Statistical Shape Modeling Using Partial Least Squares: Application to the Assessment of Myocardial Infarction

Author

Lekadir, Karim, Albà, Xènia, Pereañez, Marco, Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2016
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5. Patient Metadata-Constrained Shape Models for Cardiac Image Segmentation

Author

Pereañez, Marco, Lekadir, Karim, Albà, Xenia, Medrano-Gracia, Pau, Young, Alistair A., Frangi, Alejandro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2016
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6. Electrophysiology Model for a Human Heart with Ischemic Scar and Realistic Purkinje Network

Author

Lassila, Toni, Lange, Matthias, Porras Perez, Antonio R., Lekadir, Karim, Albà, Xènia, Piella, Gemma, Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2016
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8. Joint Clustering and Component Analysis of Correspondenceless Point Sets: Application to Cardiac Statistical Modeling

Author

Gooya, Ali, Lekadir, Karim, Alba, Xenia, Swift, Andrew J., Wild, Jim M., Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ourselin, Sebastien, editor, Alexander, Daniel C., editor, Westin, Carl-Fredrik, editor, and Cardoso, M. Jorge, editor

Published
2015
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9. Reusability of Statistical Shape Models for the Segmentation of Severely Abnormal Hearts

Author

Albà, Xènia, Lekadir, Karim, Hoogendoorn, Corné, Pereanez, Marco, Swift, Andrew J., Wild, Jim M., Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2015
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10. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

Author

Karim, Rashed, Bhagirath, Pranav, Claus, Piet, James Housden, R., Chen, Zhong, Karimaghaloo, Zahra, Sohn, Hyon-Mok, Lara Rodríguez, Laura, Vera, Sergio, Albà, Xènia, Hennemuth, Anja, Peitgen, Heinz-Otto, Arbel, Tal, Gonzàlez Ballester, Miguel A., Frangi, Alejandro F., Götte, Marco, Razavi, Reza, Schaeffter, Tobias, and Rhode, Kawal

Published
2016
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11. Personalized Modeling of Cardiac Electrophysiology Using Shape-Based Prediction of Fiber Orientation

Author

Lekadir, Karim, Pashaei, Ali, Hoogendoorn, Corné, Pereanez, Marco, Albà, Xènia, Frangi, Alejandro F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2014
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12. Healthy and Scar Myocardial Tissue Classification in DE-MRI

Author

Albà, Xènia, Figueras i Ventura, Rosa M., Lekadir, Karim, Frangi, Alejandro F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2013
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18. Automated cardiac MR image analysis for population imaging

Author

Albà, Xènia, Frangi Caregnato, Alejandro, Lekadir, Karim, Figueras, Rosa M., and Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Subjects
Segmentació amb talls de gràfics, Large-scale studies, Segmentation with graph cuts, Models estadístics de forma, Imatge mèdica, Estudis a gran escala, Magnetic resonance imaging, Automatic image parsing, Imatges per ressonància magnètica, Anàlisi automàtica de la imatge, Medical imaging, Statistical shape models, Segmentació cardíaca, Cardiac segmentation
Abstract

La pràctica clínica i la investigació generen grans quantitats de registres mèdics, incloent-hi imatges. No obstant això, molta informació que podria millorar l'assistència sanitària continua sent inaccessible. Es necessiten noves eines per processar dades a gran escala que tinguin en compte la variabilitat en l'anatomia i la fisiopatologia. En aquesta tesi, es presenten nous procediments per al tractament automàtic i eficient de dades mèdiques a gran escala, concretament en la segmentació d'imatges cardíaques de ressonància magnètica (RM). Les principals aportacions d'aquesta tesi permeten la segmentació automàtica (i) de múltiples seqüències de RM sense necessitat d'ajustar cap paràmetre, (ii) de casos altament variables sense un coneixement previ de la patologia involucrada, i (iii) incorporant la detecció automàtica i un control de qualitat sense necessitat de cap intervenció per part de l'usuari. Totes aquestes tècniques s’han avaluat utilitzant múltiples cohorts a gran escala de diferents centres clínics i bases de dades públiques., Clinical practice and research are routinely generating large amounts of medical records, including medical images. However, valuable knowledge that could impact healthcare delivery remains currently frozen in these population cohorts. New tools are therefore necessary to process and exploit such large-scale data, taking into account in particular the unprecedented variability in anatomy and pathophysiology. In this thesis, we present new approaches for the automatic and robust processing of large-scale medical image data, focusing on the challenging segmentation of cardiac magnetic resonance images (MRI) studies. The main contributions of this thesis allow automatic segmentation (i) across multiple MRI sequences without the need for sequence-specific parameter tuning, (ii) across highly variable cases without a priori knowledge of the involved pathology, and (iii) incorporating automatic detection and quality control without the need for any user interaction. All of these techniques are demonstrated over multiple large-scale cohorts from different clinical centers and public databases.

Published
2017

20. An Algorithm for the Segmentation of Highly Abnormal Hearts Using a Generic Statistical Shape Model.

Author

Albà X, Pereañez M, Hoogendoorn C, Swift AJ, Wild JM, Frangi AF, and Lekadir K

Subjects
Algorithms, Cardiomyopathy, Hypertrophic diagnostic imaging, Cardiomyopathy, Hypertrophic pathology, Humans, Hypertension, Pulmonary diagnostic imaging, Hypertension, Pulmonary pathology, Reproducibility of Results, Cardiac Imaging Techniques methods, Heart diagnostic imaging, Models, Cardiovascular, Models, Statistical, Myocardium pathology
Abstract

Statistical shape models (SSMs) have been widely employed in cardiac image segmentation. However, in conditions that induce severe shape abnormality and remodeling, such as in the case of pulmonary hypertension (PH) or hypertrophic cardiomyopathy (HCM), a single SSM is rarely capable of capturing the anatomical variability in the extremes of the distribution. This work presents a new algorithm for the segmentation of severely abnormal hearts. The algorithm is highly flexible, as it does not require a priori knowledge of the involved pathology or any specific parameter tuning to be applied to the cardiac image under analysis. The fundamental idea is to approximate the gross effect of the abnormality with a virtual remodeling transformation between the patient-specific geometry and the average shape of the reference model (e.g., average normal morphology). To define this mapping, a set of landmark points are automatically identified during boundary point search, by estimating the reliability of the candidate points. With the obtained transformation, the feature points extracted from the patient image volume are then projected onto the space of the reference SSM, where the model is used to effectively constrain and guide the segmentation process. The extracted shape in the reference space is finally propagated back to the original image of the abnormal heart to obtain the final segmentation. Detailed validation with patients diagnosed with PH and HCM shows the robustness and flexibility of the technique for the segmentation of highly abnormal hearts of different pathologies.

Published
2016
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21. Effect of statistically derived fiber models on the estimation of cardiac electrical activation.

Author

Lekadir K, Pashaei A, Hoogendoorn C, Pereanez M, Albà X, and Frangi AF

Subjects
Animals, Cardiac Electrophysiology, Computer Simulation, Diffusion Tensor Imaging, Dogs, Image Processing, Computer-Assisted, Models, Statistical, Heart physiology, Heart Conduction System physiology, Models, Cardiovascular, Myofibrils physiology
Abstract

Myocardial fiber orientation plays a critical role in the electrical activation and subsequent contraction of the heart. To increase the clinical potential of electrophysiological (EP) simulation for the study of cardiac phenomena and the planning of interventions, accurate personalization of the fibers is a necessary yet challenging task. Due to the difficulties associated with the in vivo imaging of cardiac fiber structure, researchers have developed alternative techniques to personalize fibers. Thus far, cardiac simulation was performed mainly based on rule-based fiber models. More recently, there has been a significant interest in data-driven and statistically derived fiber models. In particular, our predictive method in [1] allows us to estimate the unknown subject-specific fiber orientation based on the more easily available shape information. The aim of this work is to estimate the effect of using such statistical predictive models for the estimation of cardiac electrical activation times and patterns. To this end, we perform EP simulations based on a database of ten canine ex vivo diffusion tensor imaging (DTI) datasets that include normal and failing cases. To assess the strength of the fiber models under varying conditions, we consider both sinus rhythm and biventricular pacing simulations. The results show that 1) the statistically derived fibers improve the estimation of the local activation times by an average of 53.7% over traditional rule-based models, and that 2) the obtained electrical activations are consistently similar to those of the DTI-based fibers.

Published
2014
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22. Statistical personalization of ventricular fiber orientation using shape predictors.

Author

Lekadir K, Hoogendoorn C, Pereanez M, Albà X, Pashaei A, and Frangi AF

Subjects
Algorithms, Animals, Dogs, Least-Squares Analysis, Heart Ventricles anatomy & histology, Imaging, Three-Dimensional methods, Models, Cardiovascular
Abstract

This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model.

Published
2014
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