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A New Semi-automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences

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Abstract

Purpose

Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio.

Methods

We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no dependence on prior geometrical information, training data, or registration from an atlas.

Results

The method was evaluated using three-dimensional ultrasound scan sequences from 18 patients from the Mazankowski Alberta Heart Institute, Edmonton, Canada, and compared to manual delineations provided by an expert cardiologist and four other registration algorithms. The segmentation approach yielded the following results over the cardiac cycle: a mean absolute difference of 1.01 (0.21) mm, a Hausdorff distance of 4.41 (1.43) mm, and a Dice overlap score of 0.93 (0.02).

Conclusion

The method performed well compared to the four other registration algorithms.

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Data availability

The data used for this manuscript is not available.

Code availability

Custom code was used for this manuscript and is not available.

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Funding

The authors would like to thank CIHR/NSERC Collaborative Health Research Projects (CHRP), NSERC Discovery Grant, Heart & Stroke Foundation of Alberta, NWT, and Nunavut and Servier Canada Inc. for providing the research funding that supported this work. The graphics processor used in this research was donated by the NVIDIA Corporation.

Author information

Authors and Affiliations

  1. Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada

    Deepa Krishnaswamy, Abhilash R. Hareendranathan, Pierre Boulanger, Michelle Noga & Kumaradevan Punithakumar

  2. Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, AB, Canada

    Deepa Krishnaswamy, Abhilash R. Hareendranathan, Pierre Boulanger, Michelle Noga & Kumaradevan Punithakumar

  3. Department of Computing Science, University of Alberta, Edmonton, AB, Canada

    Pierre Boulanger & Kumaradevan Punithakumar

  4. ABACUS, Mazankowski Alberta Heart Institute, Edmonton, AB, Canada

    Tan Suwatanaviroj & Harald Becher

Authors
  1. Deepa Krishnaswamy
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  3. Tan Suwatanaviroj
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  7. Kumaradevan Punithakumar
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Contributions

DK wrote the manuscript, software and algorithms necessary for the project, and performed all of the analysis to obtain the results. AH provided technical insight and manuscript editing. TS and HB provided the ground truth contours for the dataset. PB provided technical insight and manuscript editing. MN provided insight into the methodology, clinical expertise and significant editing of the manuscript. KP provided technical expertise, significant editing of the manuscript, including feedback on the figures and tables.

Corresponding author

Correspondence to Deepa Krishnaswamy.

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Conflicts of interest

There are no conflicts of interest or competing interests to report.

Informed Consent

No animal studies were carried out by the authors for this article. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study.

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Associate Editor Ajit P. Yoganathan oversaw the review of this article.

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Krishnaswamy, D., Hareendranathan, A.R., Suwatanaviroj, T. et al. A New Semi-automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences. Cardiovasc Eng Tech 13, 55–68 (2022). https://doi.org/10.1007/s13239-021-00547-6

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