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Intracranial Volume Quantification from 3D Photography

  • Liyun Tu
  • Antonio R. Porras
  • Scott Ensel
  • Deki Tsering
  • Beatriz Paniagua
  • Andinet Enquobahrie
  • Albert Oh
  • Robert Keating
  • Gary F. Rogers
  • Marius George Linguraru
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10550)

Abstract

3D photography offers non-invasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. However, intracranial volume (ICV) quantification is not possible with current non-invasive imaging systems in order to evaluate brain development in children with cranial pathology. The aim of this study is to develop an automated, radiation-free framework to estimate ICV. Pairs of computed tomography (CT) images and 3D photographs were aligned using registration. We used the real ICV calculated from the CTs and the head volumes from their corresponding 3D photographs to create a regression model. Then, a template 3D photograph was selected as a reference from the data, and a set of landmarks defining the cranial vault were detected automatically on that template. Given the 3D photograph of a new patient, it was registered to the template to estimate the cranial vault area. After obtaining the head volume, the regression model was then used to estimate the ICV. Experiments showed that our volume regression model predicted ICV from head volumes with an average error of 5.81 \( \pm \) 3.07% and a correlation (R2) of 0.96. We also demonstrated that our automated framework quantified ICV from 3D photography with an average error of 7.02 \( \pm \) 7.76%, a correlation (R2) of 0.94, and an average estimation error for the position of the cranial base landmarks of 11.39 \( \pm \) 4.3 mm.

Keywords

3D photography Computed tomography Intracranial volume quantification Registration 

Notes

Acknowledgements

This work was partly funded by the National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development under grant NIH R42HD081712.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Liyun Tu
    • 1
  • Antonio R. Porras
    • 1
  • Scott Ensel
    • 1
  • Deki Tsering
    • 2
  • Beatriz Paniagua
    • 3
  • Andinet Enquobahrie
    • 3
  • Albert Oh
    • 4
  • Robert Keating
    • 2
  • Gary F. Rogers
    • 4
  • Marius George Linguraru
    • 1
    • 5
  1. 1.Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Health SystemWashington DCUSA
  2. 2.Division of NeurosurgeryChildren’s National Health SystemWashington DCUSA
  3. 3.Kitware Inc.CarrboroUSA
  4. 4.Division of Plastic and Reconstructive SurgeryChildren’s National Health SystemWashington DCUSA
  5. 5.School of Medicine and Health SciencesGeorge Washington UniversityWashington DCUSA

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