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Measurement and Quantification

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AI and Big Data in Cardiology

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Abstract

This chapter deals with the clinical task of measuring and quantifying cardiac morphology and function. The chapter opens with a clinical introduction, which outlines current clinical workflows, including how they derive and make use of biomarkers as well as their weaknesses and limitations. A technical review is provided that summarises the state-of-the-art in AI for automated measurement and quantification. This technical section describes the common deep learning models that have been proposed for measurement and quantification, and also gives some specific examples of their application. A practical tutorial is provided on a simple CMR segmentation task. The chapter closes with a clinical opinion piece that speculates on the future impact of AI in this area.

Authors’ contribution:

\(\bullet \) Introduction, Opinion: BR.

\(\bullet \) Main chapter: OB, MD.

\(\bullet \) Tutorial: OB, TG.

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Notes

  1. 1.

    Confusingly, fully convolutional networks are sometimes abbreviated as FCNs, conflicting with the use of the same term for fully-connected networks. In this book we limit the use of the abbreviation FCN to fully-connected networks.

  2. 2.

    Graphics Processing Units. Training modern deep learning models typically has high computational demands and is often only made feasible by exploiting dedicated hardware on GPUs.

  3. 3.

    VGG is a widely used CNN architecture for classification problems [49].

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Acknowledgements

BR was supported by the NIHR Cardiovascular MedTech Co-operative award to the Guy’s and St Thomas’ NHS Foundation Trust and Wellcome/EPSRC Centre for Medical Engineering at Kings College London (WT 203148/Z/16/Z).

Author information

Authors and Affiliations

  1. Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621, Lyon, France

    Olivier Bernard & Thomas Grenier

  2. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Bram Ruijsink

  3. Heart and Lungs Division, University Medical Centre, Utrecht, Netherlands

    Bram Ruijsink

  4. Philips Research, Suresnes, France

    Mathieu De Craene

Authors
  1. Olivier Bernard
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  2. Bram Ruijsink
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  4. Mathieu De Craene
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Correspondence to Olivier Bernard .

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Editors and Affiliations

  1. CREATIS, Lyon 1 University, Villeurbanne Cedex, France

    Nicolas Duchateau

  2. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Andrew P. King

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Bernard, O., Ruijsink, B., Grenier, T., De Craene, M. (2023). Measurement and Quantification . In: Duchateau, N., King, A.P. (eds) AI and Big Data in Cardiology. Springer, Cham. https://doi.org/10.1007/978-3-031-05071-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-05071-8_4

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