Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets

Raja Ebsim; Ben G Faber; Fiona R Saunders; Monika R Frysz; Jenny S Gregory; Nicholas Harvey; Jonathan H Tobias; Claudia Lindner; Timothy Cootes

doi:10.1007/978-3-031-16443-9_1

Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets

Raja Ebsim^*, Ben G Faber, Fiona R Saunders, Monika R Frysz, Jenny S Gregory, Nicholas Harvey, Jonathan H Tobias, Claudia Lindner, Timothy Cootes

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

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Abstract

Osteophytes are distinctive radiographic features of osteoarthritis
(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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Nature
Pages	3-12
Number of pages	10
ISBN (Electronic)	978-3-031-16443-9
ISBN (Print)	978-3-031-16442-2
DOIs	https://doi.org/10.1007/978-3-031-16443-9_1
Publication status	Published - 16 Sept 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13435 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

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.

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Ebsim, R., Faber, B. G., Saunders, F. R., Frysz, M. R., Gregory, J. S., Harvey, N., Tobias, J. H., Lindner, C., & Cootes, T. (2022). Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13435 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-16443-9_1

Ebsim, Raja ; Faber, Ben G ; Saunders, Fiona R et al. / Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Nature, 2022. pp. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Osteophytes are distinctive radiographic features of osteoarthritis (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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note = "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: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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editor = "Linwei Wang and Qi Dou and Fletcher, \{P. Thomas\} and Stefanie Speidel and Shuo Li",

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Ebsim, R, Faber, BG, Saunders, FR, Frysz, MR, Gregory, JS, Harvey, N, Tobias, JH, Lindner, C & Cootes, T 2022, Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13435 LNCS, Springer Nature, pp. 3-12. https://doi.org/10.1007/978-3-031-16443-9_1

Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets. / Ebsim, Raja; Faber, Ben G; Saunders, Fiona R et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Nature, 2022. p. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13435 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

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AU - Harvey, Nicholas

AU - Tobias, Jonathan H

AU - Lindner, Claudia

AU - Cootes, Timothy

N1 - 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.

PY - 2022/9/16

Y1 - 2022/9/16

N2 - Osteophytes are distinctive radiographic features of osteoarthritis (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.

AB - Osteophytes are distinctive radiographic features of osteoarthritis (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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DO - 10.1007/978-3-031-16443-9_1

M3 - Conference Contribution (Conference Proceeding)

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Nature

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Ebsim R, Faber BG, Saunders FR, Frysz MR, Gregory JS, Harvey N et al. Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer Nature. 2022. p. 3-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16443-9_1

Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets

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