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Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.

Publication ,  Journal Article
Heckelman, LN; Soher, BJ; Spritzer, CE; Lewis, BD; DeFrate, LE
Published in: Sci Rep
May 12, 2022

Segmentation of medical images into different tissue types is essential for many advancements in orthopaedic research; however, manual segmentation techniques can be time- and cost-prohibitive. The purpose of this work was to develop a semi-automatic segmentation algorithm that leverages gradients in spatial intensity to isolate the patella bone from magnetic resonance (MR) images of the knee that does not require a training set. The developed algorithm was validated in a sample of four human participants (in vivo) and three porcine stifle joints (ex vivo) using both magnetic resonance imaging (MRI) and computed tomography (CT). We assessed the repeatability (expressed as mean ± standard deviation) of the semi-automatic segmentation technique on: (1) the same MRI scan twice (Dice similarity coefficient = 0.988 ± 0.002; surface distance = - 0.01 ± 0.001 mm), (2) the scan/re-scan repeatability of the segmentation technique (surface distance = - 0.02 ± 0.03 mm), (3) how the semi-automatic segmentation technique compared to manual MRI segmentation (surface distance = - 0.02 ± 0.08 mm), and (4) how the semi-automatic segmentation technique compared when applied to both MRI and CT images of the same specimens (surface distance = - 0.02 ± 0.06 mm). Mean surface distances perpendicular to the cartilage surface were computed between pairs of patellar bone models. Critically, the semi-automatic segmentation algorithm developed in this work reduced segmentation time by approximately 75%. This method is promising for improving research throughput and potentially for use in generating training data for deep learning algorithms.

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

May 12, 2022

Volume

12

Issue

1

Start / End Page

7825

Location

England

Related Subject Headings

  • Tomography, X-Ray Computed
  • Swine
  • Magnetic Resonance Imaging
  • Knee Joint
  • Image Processing, Computer-Assisted
  • Humans
  • Animals
  • Algorithms
 

Citation

APA
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ICMJE
MLA
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Heckelman, L. N., Soher, B. J., Spritzer, C. E., Lewis, B. D., & DeFrate, L. E. (2022). Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency. Sci Rep, 12(1), 7825. https://doi.org/10.1038/s41598-022-11785-6
Heckelman, Lauren N., Brian J. Soher, Charles E. Spritzer, Brian D. Lewis, and Louis E. DeFrate. “Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.Sci Rep 12, no. 1 (May 12, 2022): 7825. https://doi.org/10.1038/s41598-022-11785-6.
Heckelman LN, Soher BJ, Spritzer CE, Lewis BD, DeFrate LE. Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency. Sci Rep. 2022 May 12;12(1):7825.
Heckelman, Lauren N., et al. “Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.Sci Rep, vol. 12, no. 1, May 2022, p. 7825. Pubmed, doi:10.1038/s41598-022-11785-6.
Heckelman LN, Soher BJ, Spritzer CE, Lewis BD, DeFrate LE. Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency. Sci Rep. 2022 May 12;12(1):7825.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

May 12, 2022

Volume

12

Issue

1

Start / End Page

7825

Location

England

Related Subject Headings

  • Tomography, X-Ray Computed
  • Swine
  • Magnetic Resonance Imaging
  • Knee Joint
  • Image Processing, Computer-Assisted
  • Humans
  • Animals
  • Algorithms