Literature DB >> 23708255

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

C Tobon-Gomez1, M De Craene, K McLeod, L Tautz, W Shi, A Hennemuth, A Prakosa, H Wang, G Carr-White, S Kapetanakis, A Lutz, V Rasche, T Schaeffter, C Butakoff, O Friman, T Mansi, M Sermesant, X Zhuang, S Ourselin, H-O Peitgen, X Pennec, R Razavi, D Rueckert, A F Frangi, K S Rhode.   

Abstract

In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Year:  2013        PMID: 23708255     DOI: 10.1016/j.media.2013.03.008

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  33 in total

Review 1.  Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions.

Authors:  Kurt G Schilling; Alessandro Daducci; Klaus Maier-Hein; Cyril Poupon; Jean-Christophe Houde; Vishwesh Nath; Adam W Anderson; Bennett A Landman; Maxime Descoteaux
Journal:  Magn Reson Imaging       Date:  2018-11-29       Impact factor: 2.546

2.  Temporally diffeomorphic cardiac motion estimation from three-dimensional echocardiography by minimization of intensity consistency error.

Authors:  Zhijun Zhang; Muhammad Ashraf; David J Sahn; Xubo Song
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

3.  CardIAc: an open-source application for myocardial strain analysis.

Authors:  Ariel Hernán Curiale; Agustín Bernardo; Rodrigo Cárdenas; German Mato
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-11-16       Impact factor: 2.924

4.  A Novel Filtering Approach for 3D Harmonic Phase Analysis of Tagged MRI.

Authors:  Xiaokai Wang; Maureen L Stone; Jerry L Prince; Arnold D Gomez
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-03-02

5.  Test Suite for Image-Based Motion Estimation of the Brain and Tongue.

Authors:  Jordan Ramsey; Jerry L Prince; Arnold D Gomez
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-03-13

6.  Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images.

Authors:  Fangxu Xing; Jonghye Woo; Arnold D Gomez; Dzung L Pham; Philip V Bayly; Maureen Stone; Jerry L Prince
Journal:  IEEE Trans Med Imaging       Date:  2017-07-04       Impact factor: 10.048

Review 7.  Challenges of cardiac image analysis in large-scale population-based studies.

Authors:  Pau Medrano-Gracia; Brett R Cowan; Avan Suinesiaputra; Alistair A Young
Journal:  Curr Cardiol Rep       Date:  2015-03       Impact factor: 2.931

8.  Contour tracking in echocardiographic sequences via sparse representation and dictionary learning.

Authors:  Xiaojie Huang; Donald P Dione; Colin B Compas; Xenophon Papademetris; Ben A Lin; Alda Bregasi; Albert J Sinusas; Lawrence H Staib; James S Duncan
Journal:  Med Image Anal       Date:  2013-11-06       Impact factor: 8.545

9.  Deformable models with sparsity constraints for cardiac motion analysis.

Authors:  Yang Yu; Shaoting Zhang; Kang Li; Dimitris Metaxas; Leon Axel
Journal:  Med Image Anal       Date:  2014-03-27       Impact factor: 8.545

Review 10.  Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions.

Authors:  Ghada Zamzmi; Li-Yueh Hsu; Wen Li; Vandana Sachdev; Sameer Antani
Journal:  IEEE Rev Biomed Eng       Date:  2021-01-22
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.