Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach
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-dimens...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/11/2062 |
| _version_ | 1827676894081843200 |
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| author | Benyameen Keelson Luca Buzzatti Jakub Ceranka Adrián Gutiérrez Simone Battista Thierry Scheerlinck Gert Van Gompel Johan De Mey Erik Cattrysse Nico Buls Jef Vandemeulebroucke |
| author_facet | Benyameen Keelson Luca Buzzatti Jakub Ceranka Adrián Gutiérrez Simone Battista Thierry Scheerlinck Gert Van Gompel Johan De Mey Erik Cattrysse Nico Buls Jef Vandemeulebroucke |
| author_sort | Benyameen Keelson |
| collection | DOAJ |
| description | 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 (<i>n</i> = 5) and knee (<i>n</i> = 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. |
| first_indexed | 2024-03-10T05:34:40Z |
| format | Article |
| id | doaj.art-a67f0b85b0984c4a9a2f461bcf1b41ed |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T05:34:40Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-a67f0b85b0984c4a9a2f461bcf1b41ed2023-11-22T23:01:48ZengMDPI AGDiagnostics2075-44182021-11-011111206210.3390/diagnostics11112062Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas ApproachBenyameen Keelson0Luca Buzzatti1Jakub Ceranka2Adrián Gutiérrez3Simone Battista4Thierry Scheerlinck5Gert Van Gompel6Johan De Mey7Erik Cattrysse8Nico Buls9Jef Vandemeulebroucke10Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, BelgiumDepartment of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, BelgiumDepartment of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Campus of Savona, University of Genova, 17100 Savona, ItalyDepartment of Orthopaedic Surgery and Traumatology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, BelgiumDepartment of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDepartment of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, BelgiumDynamic 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 (<i>n</i> = 5) and knee (<i>n</i> = 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.https://www.mdpi.com/2075-4418/11/11/2062dynamic CTmotion analysismusculoskeletal imagingregistrationsegmentationmulti-atlas segmentation |
| spellingShingle | Benyameen Keelson Luca Buzzatti Jakub Ceranka Adrián Gutiérrez Simone Battista Thierry Scheerlinck Gert Van Gompel Johan De Mey Erik Cattrysse Nico Buls Jef Vandemeulebroucke Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach Diagnostics dynamic CT motion analysis musculoskeletal imaging registration segmentation multi-atlas segmentation |
| title | Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach |
| title_full | Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach |
| title_fullStr | Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach |
| title_full_unstemmed | Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach |
| title_short | Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach |
| title_sort | automated motion analysis of bony joint structures from dynamic computer tomography images a multi atlas approach |
| topic | dynamic CT motion analysis musculoskeletal imaging registration segmentation multi-atlas segmentation |
| url | https://www.mdpi.com/2075-4418/11/11/2062 |
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