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LRVRG: a local region-based variational region growing algorithm for fast mandible segmentation from CBCT images

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Abstract

Objectives

To segment the mandible from cone-beam computed tomography (CBCT) images efficiently and accurately for the 3D mandible model is essential for subsequent research and diagnosis.

Methods

This paper proposes a local region-based variational region growing algorithm, which integrates local region and shape prior to segment the mandible accurately. Firstly, we select initial seeds in the CBCT image and then calculate candidate point sets and the local region energy function of each point. If a point reduces the energy, it is selected to be a pixel of the foreground region. By multiple iterations, the mandible segmentation of the slice can be obtained. Secondly, the segmented result of the previous slice is adopted as the shape prior to the next slice until all of the slices in CBCT are segmented. At last, the final mandible model is reconstructed by the Marching Cubes algorithm.

Results

The experimental results on CBCT datasets illustrate the LRVRG algorithm can obtain satisfied 3D mandible models from CBCT images and it can solve the fuzzy problem effectively. Furthermore, quantitative comparisons with other methods demonstrate the proposed method achieves the state-of-the-art performance in mandible segmentation.

Conclusions

Experiments demonstrate that our method is efficient and accurate for the mandible model segmentation.

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Acknowledgement

This work was funded by the National Key R&D Program of China (Grant No. 2019YFC17902) and the National Natural Science Foundation of China (Grant No. 61672452, 81827804, 61972342, 81970978). 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 2008 (5). Informed consent was obtained from all patients for being included in the study. All authors have no conflict of interest. This article does not contain any studies with animal subjects performed by any of the authors.

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Author notes

  1. Yankai Jiang and Jiahong Qian authors contributed equllly and should be regarded as co-first authors.

Authors and Affiliations

  1. State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou, China

    Yankai Jiang, Jiahong Qian, Shijuan Lu, Yubo Tao & Hai Lin

  2. Innovation Center for Minimally Invasive Technique and Device, Zhejiang University, Hangzhou, China

    Yubo Tao & Hai Lin

  3. Department of Stomatology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China

    Jun Lin

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  1. Yankai Jiang
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  2. Jiahong Qian
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  4. Yubo Tao
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  5. Jun Lin
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Correspondence to Jun Lin or Hai Lin.

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Jiang, Y., Qian, J., Lu, S. et al. LRVRG: a local region-based variational region growing algorithm for fast mandible segmentation from CBCT images. Oral Radiol 37, 631–640 (2021). https://doi.org/10.1007/s11282-020-00503-5

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  • DOI: https://doi.org/10.1007/s11282-020-00503-5

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