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Left Ventricle Segmentation Using Model Fitting and Active Surfaces

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Abstract

A method to perform 4D (3D over time) seg mentation of the left ventricle of a mouse heart using a set of B mode cine slices acquired in vivo from a series of short axis scans is described. We incorporate previ ously suggested methods such as temporal propagation, the gradient vector flow active surface, superquadric models, etc. into our proposed 4D segmentation of the left ventricle. The contributions of this paper are incor poration of a novel despeckling method and the use of locally fitted superellipsoid models to provide a better initialization for the active surface segmentation algorithm. Average distances of the improved surface segmentation to a manually segmented surface through out the entire cardiac cycle and cross-sectional contours are provided to demonstrate the improvements pro duced by the proposed 4D segmentation.

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Notes

  1. Area in the image where features or structure exist but were not captured in the image.

  2. Although the speed and robustness of the algorithm could be improved by establishing a optimal search method, the optimality of the search method is beyond the scope of this paper.

  3. Determination of which search algorithm provides the best tradeoff between computational speed and accuracy is beyond the scope of this paper.

  4. The point during the cardiac cycle when the myocardium is most relaxed and the volume of the LV is at its maximum. See Agnew et al. [44] for a concise definition.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering Technology, Western Carolina University, Cullowhee, NC, 28723, USA

    Peter C. Tay

  2. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Bing Li & Scott T. Acton

  3. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA

    Chris D. Garson, Scott T. Acton & John A. Hossack

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  1. Peter C. Tay
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  2. Bing Li
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Corresponding author

Correspondence to Peter C. Tay.

Additional information

This work was supported in part by NIH NIBIB grant EB001826, US Army CDMRP grant (W81XWH-04-1-0240), and NIH NCRR RR022582 (for purchase of VisualSonics Vevo 770 scanner).

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Tay, P.C., Li, B., Garson, C.D. et al. Left Ventricle Segmentation Using Model Fitting and Active Surfaces. J Sign Process Syst Sign Image Video Technol 55, 139–156 (2009). https://doi.org/10.1007/s11265-008-0219-1

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  • DOI: https://doi.org/10.1007/s11265-008-0219-1

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