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Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images

BACKGROUND: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully...

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Autores principales: Bugeja, Jessica M., Xia, Ying, Chandra, Shekhar S., Murphy, Nicholas J., Eyles, Jillian, Spiers, Libby, Crozier, Stuart, Hunter, David J., Fripp, Jurgen, Engstrom, Craig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511434/
https://www.ncbi.nlm.nih.gov/pubmed/36185062
http://dx.doi.org/10.21037/qims-22-332
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author Bugeja, Jessica M.
Xia, Ying
Chandra, Shekhar S.
Murphy, Nicholas J.
Eyles, Jillian
Spiers, Libby
Crozier, Stuart
Hunter, David J.
Fripp, Jurgen
Engstrom, Craig
author_facet Bugeja, Jessica M.
Xia, Ying
Chandra, Shekhar S.
Murphy, Nicholas J.
Eyles, Jillian
Spiers, Libby
Crozier, Stuart
Hunter, David J.
Fripp, Jurgen
Engstrom, Craig
author_sort Bugeja, Jessica M.
collection PubMed
description BACKGROUND: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients. METHODS: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate ‘healthy’ femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients. RESULTS: There was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm(3), U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm(2), t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001). CONCLUSIONS: The fully automated 3D CamMorph pipeline developed in the present study successfully segmented and measured cam morphology from clinical MR images of the hip in male and female patients with differing FAI severity and pathoanatomical characteristics.
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spelling pubmed-95114342022-10-01 Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images Bugeja, Jessica M. Xia, Ying Chandra, Shekhar S. Murphy, Nicholas J. Eyles, Jillian Spiers, Libby Crozier, Stuart Hunter, David J. Fripp, Jurgen Engstrom, Craig Quant Imaging Med Surg Original Article BACKGROUND: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients. METHODS: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate ‘healthy’ femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients. RESULTS: There was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm(3), U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm(2), t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001). CONCLUSIONS: The fully automated 3D CamMorph pipeline developed in the present study successfully segmented and measured cam morphology from clinical MR images of the hip in male and female patients with differing FAI severity and pathoanatomical characteristics. AME Publishing Company 2022-10 /pmc/articles/PMC9511434/ /pubmed/36185062 http://dx.doi.org/10.21037/qims-22-332 Text en 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Bugeja, Jessica M.
Xia, Ying
Chandra, Shekhar S.
Murphy, Nicholas J.
Eyles, Jillian
Spiers, Libby
Crozier, Stuart
Hunter, David J.
Fripp, Jurgen
Engstrom, Craig
Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title_full Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title_fullStr Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title_full_unstemmed Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title_short Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
title_sort automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3d magnetic resonance images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511434/
https://www.ncbi.nlm.nih.gov/pubmed/36185062
http://dx.doi.org/10.21037/qims-22-332
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