Automatic segmentation of hip osteophytes in DXA scans sing U-nets
Automatic segmentation of hip osteophytes in DXA scans sing U-nets
Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71±0.19) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66±0.24) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64±0.24) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA.
Automated osteoarthritis risk assessment, Computational anatomy, Osteophytes detection, Osteophytes segmentation, U-Nets
3-12
Ebsim, Raja
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Faber, Benjamin
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Saunders, Fiona R.
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Frysz, Monika
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Gregory, Jennifer S.
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Harvey, Nicholas
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Tobias, Jonathan H.
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Lindner, Claudia
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Cootes, Timothy
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16 September 2022
Ebsim, Raja
fa3d2f2c-9d77-4b95-b0ff-c34b57142381
Faber, Benjamin
85a38e7f-74a4-4ba7-a985-a1cff3392ed0
Saunders, Fiona R.
a51cc79d-0928-4ab6-a479-3972c974670b
Frysz, Monika
bda9e219-ca28-43e4-babd-81f2d91ca3e4
Gregory, Jennifer S.
6995d8fa-b32b-4f7c-aa15-8146acb4fd67
Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Tobias, Jonathan H.
514342d7-3491-4a7b-bbeb-b00dcf244daa
Lindner, Claudia
03ee5726-0741-4170-8375-659292641028
Cootes, Timothy
f82f878a-ab1d-426c-9510-afa3f6de7aef
Ebsim, Raja, Faber, Benjamin, Saunders, Fiona R., Frysz, Monika, Gregory, Jennifer S., Harvey, Nicholas, Tobias, Jonathan H., Lindner, Claudia and Cootes, Timothy
(2022)
Automatic segmentation of hip osteophytes in DXA scans sing U-nets.
Wang, Linwei, Dou, Qi, Thomas Fletcher, P., Speidel, Stefanie and Li, Shuo
(eds.)
In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings.
vol. 13435 LNCS,
Springer.
.
(doi:10.1007/978-3-031-16443-9_1).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71±0.19) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66±0.24) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64±0.24) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA.
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More information
e-pub ahead of print date: 16 September 2022
Published date: 16 September 2022
Additional Information:
Funding Information:
Acknowledgements. RE, FS and MF are funded by a Wellcome Trust collaborative award (reference number 209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). CL was funded by the MRC, UK (MR/S00405X/1) as well as a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (223267/Z/21/Z). NCH is supported by the UK Medical Research Council [MC_PC_21003; MC_PC_21001].
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords:
Automated osteoarthritis risk assessment, Computational anatomy, Osteophytes detection, Osteophytes segmentation, U-Nets
Identifiers
Local EPrints ID: 473217
URI: http://eprints.soton.ac.uk/id/eprint/473217
ISSN: 0302-9743
PURE UUID: 2fae0940-a338-4335-8e7c-183d1a3fd991
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Date deposited: 12 Jan 2023 18:02
Last modified: 17 Mar 2024 02:59
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Contributors
Author:
Raja Ebsim
Author:
Benjamin Faber
Author:
Fiona R. Saunders
Author:
Monika Frysz
Author:
Jennifer S. Gregory
Author:
Jonathan H. Tobias
Author:
Claudia Lindner
Author:
Timothy Cootes
Editor:
Linwei Wang
Editor:
Qi Dou
Editor:
P. Thomas Fletcher
Editor:
Stefanie Speidel
Editor:
Shuo Li
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