Catalina Tobon‐Gomez

King's College London

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Alejandro F. Frangi

University of Manchester

Kawal Rhode

King's College London

Reza Razavi

King's College London

Constantine Butakoff

SOM Biotech (Spain)

Marina Huguet

Hospital Quirón Teknon

Sébastien Ourselin

King's College London

Bart Bijnens

Institut Català de Ciències del Clima

Rashed Karim

King's College London

Tobias Schaeffter

Physikalisch-Technische Bundesanstalt

Yefeng Zheng

Tencent (China)

Cooperative Institutions

King's College London

St Thomas' Hospital

University College London

Guy's and St Thomas' NHS Foundation Trust

Pompeu Fabra University

Inserm

British Heart Foundation

Laboratoire d’Imagerie Biomédicale

Imperial College London

Institut national de recherche en informatique et en automatique

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Quantification of Gaps in Ablation Lesions Around the Pulmonary Veins in Delayed Enhancement MRI

Lecture notes in computer science (2015)

Marta Nuñez‐Garcia Catalina Tobon‐Gomez Kawal Rhode Bart Bijnens Óscar Cámara

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Personalized Modeling Pipeline for Left Atrial Electromechanics

Computing in cardiology (2016)

Thomas E. Fastl Catalina Tobon‐Gomez William A Crozier John Whitaker Ronak Rajani

Atrial fibrillation (AF) is a supraventricular tachyarrhythmia characterized by uncoordinated atrial activation with consequent deterioration of mechanical function.Personalized computational modeling provides a novel framework for integrating and interpreting the combined role of atrial electrophysiology and mechanics in AF development and sustenance.Coronary computed tomography angiography data were segmented using a threshold-based approach and the smoothed voxel representation was discretized into a high-resolution tetrahedral finite element (FE) mesh.To estimate the complex left atrial fiber architecture, individual fiber fields were generated according to morphological data on the endo-and epicardial surfaces based on local solutions of Laplace's equation and transmurally interpolated to all tetrahedral elements.Personalized geometrical models included the heterogeneous thickness distribution of the left atrial myocardium and subsequent discretization led to high-fidelity tetrahedral FE meshes.The novel algorithm for (automated) incorporation of the left atrial fiber architecture provided a realistic estimate of the atrial microstructure and was able to qualitatively capture all important fiber bundles.The established modeling pipeline provides a robust framework for the rapid development of personalized model cohorts and facilitates simulations of atrial electromechanics.

Electromechanics

10.22489/cinc.2016.068-193

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Citations (5)

Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints

Magnetic Resonance in Medicine (2013)

Xènia Albà Rosa Ventura Karim Lekadir Catalina Tobon‐Gomez Corné Hoogendoorn

Purpose Magnetic resonance imaging (MRI), specifically late‐enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data. Methods A generic rule‐based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject‐ and study‐specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness. Results The method was evaluated on late‐enhanced MRI images from a 20‐patient in‐house database, and on cine‐MRI images from a 15‐patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was and for late‐enhanced MRI and cine‐MRI, respectively. The method was also compared favorably to a three‐dimensional Active Shape Model approach. Conclusion The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility. Magn Reson Med 72:1775–1784, 2014. © 2013 Wiley Periodicals, Inc.

Cut

Initialization

Robustness

Sørensen–Dice coefficient

Hausdorff distance

10.1002/mrm.25079

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Citations (40)

A Multimodal Database for the 1 st Cardiac Motion Analysis Challenge

Lecture notes in computer science (2012)

Catalina Tobon‐Gomez Mathieu De Craene Annette Dahl Stam Kapetanakis G Carr-White

Cardiac cycle

DICOM

Real-time MRI

Fast spin echo

10.1007/978-3-642-28326-0_4

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Citations (16)

Influence of dynamic obstruction and hypertrophy location on diastolic function in hypertrophic cardiomyopathy

Journal of Cardiovascular Medicine (2014)

Gustavo Avegliano Juan Pablo Costabel Marina Huguet Jorge Thierer Marcelo Trivi

Hypertrophic cardiomyopathy (HCM) is a disease with marked genetic and phenotypic heterogeneity. It is well known that obstructive septal forms of this disease entail worse clinical outcome compared with nonobstructive septal and apical forms. The objective of this study was to analyze the differences in left ventricular diastolic function in different subgroups of HCMs and to assess the influence of the location of myocardial hypertrophy and the presence of dynamic obstruction on impairment of diastolic function and its correlation with the clinical status.We studied 86 patients with HCM; 27 with the obstructive asymmetric septal type (OAS), 37 with the nonobstructive asymmetric septal type (NOAS) and 22 with apical hypertrophic cardiomyopathy (ApHCM). Patients underwent conventional and tissue Doppler echocardiography and were assessed applying the latest recommendations regarding diastolic dysfunction. Cardiac magnetic resonance was used to study the various morphologic subtypes and quantify left ventricular mass (LVM).The early diastolic annular velocity (e') was significantly lower in OAS with a median of 5 cm/s compared with NOAS with 7 cm/s and ApHCM with 7.5 cm/s (P = 0.0002), and the E/e' ratio was 8.5 in ApHCM, 10 in NOAS and 14 in OAS (P = 0.0001); no significant differences were found in LVM or maximal wall thickness.In HCM, the location of left ventricular hypertrophy and the presence of dynamic obstruction affect the degree of diastolic dysfunction; impairment is greater in patients with the OAS type, and markedly less in patients with apical involvement.

10.2459/jcm.0b013e3283638093

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Citations (4)

Automatic Construction of 3D-ASM Intensity Models by Simulating Image Acquisition: Application to Myocardial Gated SPECT Studies

IEEE Transactions on Medical Imaging (2008)

Catalina Tobon‐Gomez Constantine Butakoff S Aguadé Federico M. Sukno Glòria Sánchez

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.

Image registration

Solid modeling

Landmark

Gated SPECT

10.1109/tmi.2008.2004819

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Citations (34)

Temporal Diffeomorphic Free Form Deformation (TDFFD) Applied to Motion and Deformation Quantification of Tagged MRI Sequences

Lecture notes in computer science (2012)

Mathieu De Craene Catalina Tobon‐Gomez Constantine Butakoff Nicolás Duchateau Gemma Piella

Tracking (education)

Similarity (geometry)

Match moving

Diffeomorphism

Motion analysis

Temporal resolution

10.1007/978-3-642-28326-0_7

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Citations (26)

Left Atrial Segmentation Challenge 2013: MRI results

Figshare (2015)

Catalina Tobon‐Gomez Arjan J. Geers Jörg Peters Jürgen Weese Karen Pinto

This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13). LASC'13 was part of the STACOM'13 workshop, held in conjunction with MICCAI'13. Seven international research groups, comprising 11 algorithms, participated in the challenge. For a detailed report, please refer to: Tobon-Gomez C, Geers AJ, Peters, J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y, Zuluaga, MA, Betancur J, Ayache N, Chikh MA, Dillenseger J-L, Kelm BM, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R, Rhode KS. Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets. IEEE Transactions on Medical Imaging, 34(7):1460–1473, 2015. The challenge is also featured on http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/The data and code of the challenge have been made publicly available to serve as a benchmark for left atrial segmentation algorithms. Code is hosted on https://github.com/catactg/lasc Feel free to contact us with any questions. This fileset consists of results from algorithms evaluated during LASC'13. Included are the binary mask provided by the participant and the output files of lasc/code/lasc_benchmark.py in the code repository. dm_body.csv: Dice metric of the LA body dm_pvs.csv: Dice metric of the PVs mask.mhd + mask.raw: Binary mask provided by segmentation algorithm mesh.vtp: Surface mesh extracted from mask.mhd using marching cubes s2s_body.csv: Surface-to-surface metric of the LA body s2s_pvs.csv: Surface-to-surface metric of the PVs std_gt2seg.vtp: Surface mesh with distance from GT to segmentation (as pointdata array) std_mesh.vtp: Surface mesh after standardisation std_seg2gt.vtp: Surface mesh with distance from segmentation to GT (as pointdata array) std.mhd + std.zraw: Image representation of segmentation with labels copied from std_mesh.vtp

10.6084/m9.figshare.1492974

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Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: Feasibility and clinical application

European Journal of Radiology (2018)

Ayla Urbaneja Jacques De Verbizier Anne-Sophie Formery Catalina Tobon‐Gomez L. Nace

Gold standard (test)

10.1016/j.ejrad.2018.11.011

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Citations (33)

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

Medical Image Analysis (2013)