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Automated Measurement of Pelvic Incidence from X-Ray Images

  • Robert Korez
  • Michael Putzier
  • Tomaž VrtovecEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)

Abstract

One of the most important parameters of sagittal pelvic alignment is the pelvic incidence (PI), which is commonly measured from sagittal X-ray images of the pelvis as the angle between the line connecting the midpoint of the femoral head centers with the center of the sacral endplate, and the line orthogonal to the sacral endplate. In this paper, we present the results of a fully automated measurement of PI from X-ray images that is based on the deep learning technologies. In each sagittal X-ray image of the pelvis, regions of interest (sacral endplate and both femoral heads) are first automatically defined, and then landmarks are detected within these regions, i.e. the anterior edge, the center and the posterior edge of the sacral endplate that define the line of the sacral endplate inclination, and the centers of both femoral heads with the corresponding midpoint representing the hip axis. From the hip axis, and the line along the sacral endplate and its center, PI is computed. Measurements were performed on X-ray pelvic images from 38 subjects (15 males/23 females; mean age 71.1 years), and statistical analysis of reference manual and fully automated measurements revealed a relatively good agreement, with the mean absolute difference ± standard deviation of \(5.1\,{\pm }\,4.4^\circ \) and Pearson correlation coefficient of \(R\,=\,0.82\) (p-value below \(10^{-6}\)), with the paired t-test revealing no statistically significant differences (p-value above 0.05). The differences between reference manual and fully automated measurements were within the repeatability and reliability of manual measurements, indicating that PI can be accurately determined by the proposed fully automated approach.

Keywords

Pelvic incidence X-ray imaging Deep learning 

Notes

Acknowledgements

This work was supported by Raylytic GmbH, Leipzig, Germany, partly by the Slovenian Research Agency under grants P2-0232 and J2-7118, and partly by the German Research Foundation (DFG) under project number PU 510/2-1.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Charité University HospitalBerlinGermany

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