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EM Segmentation of the Distal Femur and Proximal Tibia: A High-Throughput Approach to Anatomic Surface Generation

Annals of Biomedical Engineering volume 39, pages 1555–1562 (2011)Cite this article

Abstract

Fully automated segmentation of computed tomography (CT) images remains a challenge for musculoskeletal researchers. The surfaces generated from image segmentations are valuable for surgical evaluation and planning. Previously, we demonstrated the expectation maximization (EM) algorithm as a semi-automated method of bone segmentation from CT images. In this work, we improve upon the methodology of probability map generation and demonstrate extended applicability of EM-based segmentation to the distal femur and proximal tibia using 72 CT image sets. We also compare the resulting EM segmentations to manual tracings using overlap metrics and time. In the case of the distal femur, the resulting quality metrics had mean values of 0.91 and 0.95 for the Jaccard and Dice metrics, respectively. For the proximal tibia, the Jaccard and Dice metrics were 0.90 and 0.95, respectively. The EM segmentation method was 8 times faster than the average manual segmentation and required less than 4% of the human rater time. Overall, the EM algorithm offers reliable image segmentations with an increased efficiency in comparison to manual segmentation techniques.

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Acknowledgments

The authors would like to acknowledge DonJoy Orthopaedics (Carlsbad, CA) for providing the cadaveric specimens and the Multicenter Orthopaedic Outcomes Network Knee group (MOON—based at Vanderbilt University) for providing the CT scans; and Dr. Carla Britton, MS, PhD for coordinating the CT scans for our study.

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Affiliations

  1. Carver College of Medicine, The University of Iowa, Iowa City, IA, USA

    Austin J. Ramme

  2. Department of Biomedical Engineering, 1418 Seamans Center for the Engineering Arts and Sciences, The University of Iowa, Iowa City, IA, 52242, USA

    Austin J. Ramme, Amy J. Criswell, Vincent A. Magnotta & Nicole M. Grosland

  3. Center for Computer-Aided Design, The University of Iowa, Iowa City, IA, USA

    Austin J. Ramme, Amy J. Criswell, Vincent A. Magnotta & Nicole M. Grosland

  4. Department of Orthopaedics and Rehabilitation, The University of Iowa, Iowa City, IA, USA

    Brian R. Wolf & Nicole M. Grosland

  5. Department of Radiology, The University of Iowa, Iowa City, IA, USA

    Vincent A. Magnotta

Authors
  1. Austin J. Ramme
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  2. Amy J. Criswell
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  3. Brian R. Wolf
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  4. Vincent A. Magnotta
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  5. Nicole M. Grosland
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Corresponding author

Correspondence to Nicole M. Grosland.

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Associate Editor Jing Bai oversaw the review of this article.

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Ramme, A.J., Criswell, A.J., Wolf, B.R. et al. EM Segmentation of the Distal Femur and Proximal Tibia: A High-Throughput Approach to Anatomic Surface Generation. Ann Biomed Eng 39, 1555–1562 (2011). https://doi.org/10.1007/s10439-010-0244-7

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  • DOI: https://doi.org/10.1007/s10439-010-0244-7

Keywords

  • Expectation maximization
  • 3D segmentation
  • Bone
  • Knee
  • Computed tomography