Quantifying tensor field similarity with global distributions and optimal transport

Arnold Gomez, Maureen L. Stone, Philip V. Bayly, Jerry Ladd Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Strain tensor fields quantify tissue deformation and are important for functional analysis of moving organs such as the heart and the tongue. Strain data can be readily obtained using medical imaging. However, quantification of similarity between different data sets is difficult. Strain patterns vary in space and time, and are inherently multidimensional. Also, the same type of mechanical deformation can be applied to different shapes; hence, automatic quantification of similarity should be unaffected by the geometry of the objects being deformed. In the pattern recognition literature, shapes and vector fields have been classified via global distributions. This study uses a distribution of mechanical properties (a 3D histogram), and the Wasserstein distance from optimal transport theory is used to measure histogram similarity. To evaluate the method’s consistency in matching deformations across different objects, the proposed approach was used to sort strain fields according to their similarity. Performance was compared to sorting via maximum shear distribution (a 1D histogram) and tensor residual magnitude in perfectly registered objects. The technique was also applied to correlate muscle activation to muscular contraction observed via tagged MRI. The results show that the proposed approach accurately matches deformation regardless of the shape of the object being deformed. Sorting accuracy surpassed 1D shear distribution and was on par with residual magnitude, but without the need for registration between objects.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsGabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel
PublisherSpringer Verlag
Pages428-436
Number of pages9
ISBN (Print)9783030009335
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

Fingerprint

Optimal Transport
Tensors
Tensor
Histogram
Sorting
Quantification
Functional analysis
Medical imaging
Wasserstein Distance
Transport Theory
Magnetic resonance imaging
Pattern recognition
Medical Imaging
Muscle
Functional Analysis
Chemical activation
Correlate
Sort
Registration
Pattern Recognition

Keywords

  • Organ deformation
  • Strain
  • Tagged MRI
  • Tensor fields

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gomez, A., Stone, M. L., Bayly, P. V., & Prince, J. L. (2018). Quantifying tensor field similarity with global distributions and optimal transport. In G. Fichtinger, C. Davatzikos, C. Alberola-López, A. F. Frangi, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 428-436). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11071 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00934-2_48

Quantifying tensor field similarity with global distributions and optimal transport. / Gomez, Arnold; Stone, Maureen L.; Bayly, Philip V.; Prince, Jerry Ladd.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Gabor Fichtinger; Christos Davatzikos; Carlos Alberola-López; Alejandro F. Frangi; Julia A. Schnabel. Springer Verlag, 2018. p. 428-436 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11071 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gomez, A, Stone, ML, Bayly, PV & Prince, JL 2018, Quantifying tensor field similarity with global distributions and optimal transport. in G Fichtinger, C Davatzikos, C Alberola-López, AF Frangi & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11071 LNCS, Springer Verlag, pp. 428-436, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 9/16/18. https://doi.org/10.1007/978-3-030-00934-2_48
Gomez A, Stone ML, Bayly PV, Prince JL. Quantifying tensor field similarity with global distributions and optimal transport. In Fichtinger G, Davatzikos C, Alberola-López C, Frangi AF, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 428-436. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-00934-2_48
Gomez, Arnold ; Stone, Maureen L. ; Bayly, Philip V. ; Prince, Jerry Ladd. / Quantifying tensor field similarity with global distributions and optimal transport. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Gabor Fichtinger ; Christos Davatzikos ; Carlos Alberola-López ; Alejandro F. Frangi ; Julia A. Schnabel. Springer Verlag, 2018. pp. 428-436 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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