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A Multi-step Machine Learning Approach for Short Axis MR Images Segmentation

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Functional Imaging and Modeling of the Heart (FIMH 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12738))

Abstract

Segmentation of cardiac magnetic resonance (cMR) images is often the first step necessary to compute common diagnostic biomarkers, such as myocardial mass and left ventricle (LV) ejection fraction. Often image segmentation and analysis require significant, time-consuming user input. Machine learning has been increasingly adopted to automatically segment medical images to lessen the burden on image segmentation and image analysis for model construction and validation. In this work we present a multi-step machine learning approach to segment short axis cMR images based on a heart locator and the weighted average of 2D and 2D++ UNets. The presence of a heart locator led to more accurate results and allowed to increase the neural network training batch size. Finally, the obtained segmentations are post-processed using spline interpolation and the Loop scheme to generate left ventricular endocardial and epicardial surfaces at the end of diastole and end of systole.

Supported by the University of Central Florida, Mechanical and Aerospace Engineering Department.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Andre Von Zuben, Kylie Heckman, Felipe A. C. Viana & Luigi E. Perotti

Authors
  1. Andre Von Zuben
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  2. Kylie Heckman
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  3. Felipe A. C. Viana
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  4. Luigi E. Perotti
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Corresponding author

Correspondence to Andre Von Zuben .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Daniel B. Ennis

  2. University of Central Florida, Orlando, FL, USA

    Luigi E. Perotti

  3. Stanford University, Stanford, CA, USA

    Vicky Y. Wang

Appendix

Appendix

A complete prediction from the framework shown in Fig. 2 is illustrated in Fig. 7 using a representative midventricular slice. This example highlights the role of the weighted average approach as an insurance policy, as well as the spline as a way to further smooth the final LV contour.

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Von Zuben, A., Heckman, K., Viana, F.A.C., Perotti, L.E. (2021). A Multi-step Machine Learning Approach for Short Axis MR Images Segmentation. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-78710-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78709-7

  • Online ISBN: 978-3-030-78710-3

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