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Computer-assisted contralateral side comparison of the ankle joint using flat panel technology

PURPOSE: Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-i...

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Autores principales: Thomas, Sarina, Kausch, Lisa, Kunze, Holger, Privalov, Maxim, Klein, André, Barbari, Jan El, Martin Vicario, Celia, Franke, Jochen, Maier-Hein, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134308/
https://www.ncbi.nlm.nih.gov/pubmed/33877526
http://dx.doi.org/10.1007/s11548-021-02329-w
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author Thomas, Sarina
Kausch, Lisa
Kunze, Holger
Privalov, Maxim
Klein, André
Barbari, Jan El
Martin Vicario, Celia
Franke, Jochen
Maier-Hein, Klaus
author_facet Thomas, Sarina
Kausch, Lisa
Kunze, Holger
Privalov, Maxim
Klein, André
Barbari, Jan El
Martin Vicario, Celia
Franke, Jochen
Maier-Hein, Klaus
author_sort Thomas, Sarina
collection PubMed
description PURPOSE: Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation. METHOD: In this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment. RESULTS: Experiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of [Formula: see text] mm and a median angular error between 2.98[Formula: see text] and 3.71[Formula: see text] for the plane normals. CONCLUSION: Standard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery.
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spelling pubmed-81343082021-05-24 Computer-assisted contralateral side comparison of the ankle joint using flat panel technology Thomas, Sarina Kausch, Lisa Kunze, Holger Privalov, Maxim Klein, André Barbari, Jan El Martin Vicario, Celia Franke, Jochen Maier-Hein, Klaus Int J Comput Assist Radiol Surg Original Article PURPOSE: Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation. METHOD: In this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment. RESULTS: Experiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of [Formula: see text] mm and a median angular error between 2.98[Formula: see text] and 3.71[Formula: see text] for the plane normals. CONCLUSION: Standard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery. Springer International Publishing 2021-04-20 2021 /pmc/articles/PMC8134308/ /pubmed/33877526 http://dx.doi.org/10.1007/s11548-021-02329-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Thomas, Sarina
Kausch, Lisa
Kunze, Holger
Privalov, Maxim
Klein, André
Barbari, Jan El
Martin Vicario, Celia
Franke, Jochen
Maier-Hein, Klaus
Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title_full Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title_fullStr Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title_full_unstemmed Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title_short Computer-assisted contralateral side comparison of the ankle joint using flat panel technology
title_sort computer-assisted contralateral side comparison of the ankle joint using flat panel technology
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134308/
https://www.ncbi.nlm.nih.gov/pubmed/33877526
http://dx.doi.org/10.1007/s11548-021-02329-w
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