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Artificially enriching the training dataset of statistical shape models via constrained cage-based deformation

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Abstract

The construction of a powerful statistical shape model (SSM) requires a rich training dataset that includes the large variety of complex anatomical topologies. The lack of real data causes most SSMs unable to generalize possible unseen instances. Artificial enrichment of training data is one of the methods proposed to address this issue. In this paper, we introduce a novel technique called constrained cage-based deformation (CCBD), which has the ability to produce unlimited artificial data that promises to enrich variability within the training dataset. The proposed method is a two-step algorithm: in the first step, it moves a few handles together, and in the second step transfers the displacements of these handles to the base mesh vertices to generate a real new instance. The evaluation of statistical characteristics of the CCBD confirms that our proposed technique outperforms notable data-generating methods quantitatively, in terms of the generalization ability, and with respect to specificity.

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Acknowledgements

The authors gratefully acknowledge the support and generosity of Fanavaran Jarahyar Sharif Co. for providing Femur dataset that without which the present study could not have been completed. We are also immensely grateful to Seyed Mohammad Reza Noori for comments that significantly improved the manuscript.

Funding

This work was supported by the medical school, Tehran University of Medical Science in Grant Number 28345 and also by the Iran National Science Foundation in Grant Number 92011635.

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Authors and Affiliations

  1. Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences, Tehran, Iran

    Samaneh Alimohamadi Gilakjan & Alireza Ahmadian

  2. Research Center for Biomedical Technologies and Robotics, Imam Khomeini Hospital Complex, Keshavarz Blvd, Tehran, Iran

    Samaneh Alimohamadi Gilakjan & Alireza Ahmadian

  3. Control & Intelligent Processing, Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Javad Hasani Bidgoli & Reza Aghaizadeh Zorofi

Authors
  1. Samaneh Alimohamadi Gilakjan
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  2. Javad Hasani Bidgoli
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  3. Reza Aghaizadeh Zorofi
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  4. Alireza Ahmadian
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Correspondence to Alireza Ahmadian.

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Alimohamadi Gilakjan, S., Hasani Bidgoli, J., Aghaizadeh Zorofi, R. et al. Artificially enriching the training dataset of statistical shape models via constrained cage-based deformation. Australas Phys Eng Sci Med 42, 573–584 (2019). https://doi.org/10.1007/s13246-019-00759-0

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  • DOI: https://doi.org/10.1007/s13246-019-00759-0

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