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Automated motion analysis of bony joint structures from dynamic computer tomography images : A multi-atlas approach

Keelson, Benyameen ; Buzzatti, Luca ; Ceranka, Jakub ; Gutiérrez, Adrián ; Battista, Simone LU orcid ; Scheerlinck, Thierry ; Van Gompel, Gert ; De Mey, Johan ; Cattrysse, Erik and Buls, Nico , et al. (2021) In Diagnostics 11(11). p.1-17
Abstract

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the... (More)

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1◦. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

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publishing date
type
Contribution to journal
publication status
published
keywords
Dynamic CT, Motion analysis, Multi-atlas segmentation, Musculoskeletal imaging, Registration, Segmentation
in
Diagnostics
volume
11
issue
11
article number
2062
pages
1 - 17
publisher
MDPI AG
external identifiers
  • scopus:85118896469
  • pmid:34829409
ISSN
2075-4418
DOI
10.3390/diagnostics11112062
language
English
LU publication?
no
additional info
Funding Information: Funding: This research was funded by an Interdisciplinary Research Project grant from Vrije Univer-siteit Brussel IRP10 (1 July 2016–30 June 2021). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
id
93e2cbba-767e-44fe-b917-9cb5406da5d8
date added to LUP
2021-11-22 12:44:10
date last changed
2024-06-15 21:01:13
@article{93e2cbba-767e-44fe-b917-9cb5406da5d8,
  abstract     = {{<p>Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1<sup>◦</sup>. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.</p>}},
  author       = {{Keelson, Benyameen and Buzzatti, Luca and Ceranka, Jakub and Gutiérrez, Adrián and Battista, Simone and Scheerlinck, Thierry and Van Gompel, Gert and De Mey, Johan and Cattrysse, Erik and Buls, Nico and Vandemeulebroucke, Jef}},
  issn         = {{2075-4418}},
  keywords     = {{Dynamic CT; Motion analysis; Multi-atlas segmentation; Musculoskeletal imaging; Registration; Segmentation}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{1--17}},
  publisher    = {{MDPI AG}},
  series       = {{Diagnostics}},
  title        = {{Automated motion analysis of bony joint structures from dynamic computer tomography images : A multi-atlas approach}},
  url          = {{http://dx.doi.org/10.3390/diagnostics11112062}},
  doi          = {{10.3390/diagnostics11112062}},
  volume       = {{11}},
  year         = {{2021}},
}