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A novel sagittal craniosynostosis classification system based on multi-view learning algorithm

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Abstract

Sagittal craniosynostosis (CSO) occurs when the sagittal suture of a growing child’s skull is fused prematurely. Surgery, which involves re-moving the affected bones and increasing the volume of the cranium by repositioning the bone segments or using external forces to guide, is the primary treatment for CSO. For preparation of the surgery, physicians usually classify sagittal CSO subtypes by examining the reconstruction of skulls from CT images or the laser scans of the patients’ 3D skull. We have proposed an automated sagittal CSO classification algorithm based on features extracted from projected 3D skull images. However, these features would become invalid if they were extracted from irregular skulls or skulls with completely closed sutures. In order to tackle this problem, we proposed a 3D skull multi-view images-based algorithm to classify the subtypes of sagittal CSO, like the physicians diagnosing these 3D reconstruction images of skulls or laser scans. Our innovations include: 1) the first to propose a multi-view learning algorithm to classify the subtypes of sagittal CSO cases, 2) 3D rendering and a patch-based erosion method are adopted for data augmentation to keep the original ratio and shape of the images, and 3) utilizing a transfer leaning training strategy to train the convolutional neural network (CNN) and a multi-view-based prediction strategy for classifying the cases.

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Acknowledgements

This work was supported in part by the National Institutes of Health (NIH) under Grant 1R01DE027027-04 and Grant 1U01 AR069395-04 (X.Z).

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Authors and Affiliations

  1. School of Biomedical Informatics, The University of Texas Scientific Center at Houston, Houston, TX, 77030, USA

    Lei You, Yang Deng, Guangming Zhang, Yanfei Wang & Xiaobo Zhou

  2. School of Biomedical Enginmeering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China

    Yang Deng

  3. Department of Plastic and Reconstructive Surgery, Wake Forest Medical School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

    Griffin Patrick Bins, Christopher Michael Runyan & Lisa David

Authors
  1. Lei You
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  2. Yang Deng
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  3. Guangming Zhang
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  4. Yanfei Wang
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  6. Christopher Michael Runyan
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  7. Lisa David
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  8. Xiaobo Zhou
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Correspondence to Xiaobo Zhou.

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You, L., Deng, Y., Zhang, G. et al. A novel sagittal craniosynostosis classification system based on multi-view learning algorithm. Neural Comput & Applic 34, 14427–14434 (2022). https://doi.org/10.1007/s00521-022-07310-5

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  • DOI: https://doi.org/10.1007/s00521-022-07310-5

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