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3D Motion Analysis in MRI Using a Multi-objective Evolutionary k-means Clustering

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Applications of Evolutionary Computation (EvoApplications 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14634))

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Abstract

Many studies focused on gastric motility require the use of synthetic tracers to map the motion of content. Our study instead takes advantage of an unusual MRI acquisition protocol, combined with multi-objective optimised clustering to map the motion of food (peas, a natural ‘tracer’) in a human stomach. We chose NSGA-II to optimise the starting positions for a modified k-means to create optimum clusters. We compared our optimisation approach with a purely random approach that took an equal amount of processing time. Since we have no ground truth available, we have created alternative measures to evaluate our solutions: if the resulting pea velocities are within an expected range, and if each pea’s motion is correlated with neighbouring peas. We found that the optimised version has a significant improvement over the purely random search. Furthermore, we found many interesting food motion behaviours, such as correlated pea motion and more complex motion dynamics such as collision. Overall we found that the combined optimisation and clustering approach produced interesting findings relating to food dynamics in a human stomach.

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Acknowledgements

This work was partially supported by a STSM Grant from COST Action CA15118 (FoodMC) and the MRI data used in this study were collected at CEA-SHFJ with the support of IR4M CNRS/Orsay University (Xavier Maître and Luc Darrasse) in the framework of the IDI/Paris Saclay PhD of Daniela Freitas.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Bangor University, Bangor, UK

    Conor Spann

  2. INRAE-UMR518, AgroParisTech, Univ Paris-Saclay, 91120, Palaiseau, France

    Evelyne Lutton

  3. LLB-CEA, CNRS-UMR12, Univ Paris-Saclay, 91190, Gif-sur-Yvette, France

    François Boué

  4. Scientific Computing Department, Science Technology Facilities Council, UK Research and Innovation, Daresbury, UK

    Franck Vidal

Authors
  1. Conor Spann
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  2. Evelyne Lutton
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  3. François Boué
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  4. Franck Vidal
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Corresponding author

Correspondence to Conor Spann .

Editor information

Editors and Affiliations

  1. University of York, York, UK

    Stephen Smith

  2. University of Coimbra, Coimbra, Portugal

    João Correia

  3. University of Málaga, Málaga, Spain

    Christian Cintrano

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Ethical Approval

The study protocol has been approved by the Ethics Committee Lyon Sud-Est IV, and it has been registered in the Clinical Trial Registry (clinicaltrials.gov; NCT03265392) (see https://clinicaltrials.gov/ct2/show/NCT03265392). All volunteers gave their written informed consent after being provided with oral and written information about the aims and protocol of the study.

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Spann, C., Lutton, E., Boué, F., Vidal, F. (2024). 3D Motion Analysis in MRI Using a Multi-objective Evolutionary k-means Clustering. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14634. Springer, Cham. https://doi.org/10.1007/978-3-031-56852-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-56852-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56851-0

  • Online ISBN: 978-3-031-56852-7

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