A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility

Faezeh Moshfeghifar; Max Kragballe Nielsen; Jose David Tascon Vidarte; Sune Darkner; Kenny Erleben

doi:10.1007/978-3-031-09327-2_11

A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility

Faezeh Moshfeghifar^*, Max Kragballe Nielsen, Jose David Tascon Vidarte, Sune Darkner, Kenny Erleben

^*Corresponding author af dette arbejde

Image Analysis, Computational Modelling and Geometry

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Abstract

We present a method to generate subject-specific cartilage for the hip joint. Given bone geometry, our approach is agnostic to image modality, creates conforming interfaces, and is well suited for finite element analysis. We demonstrate our method on ten hip joints showing anatomical shape consistency and well-behaved stress patterns. Our method is fast and may assist in large-scale biomechanical population studies of the hip joint when manual segmentation or training data is not feasible.

Originalsprog	Engelsk
Titel	Computational Biomechanics for Medicine : Towards Translation and Better Patient Outcomes
Forlag	Springer
Publikationsdato	2022
Sider	155.169
ISBN (Trykt)	978-3-031-09326-5
ISBN (Elektronisk)	978-3-031-09327-2
DOI	https://doi.org/10.1007/978-3-031-09327-2_11
Status	Udgivet - 2022
Begivenhed	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Varighed: 18 sep. 2022 → 22 sep. 2022

Konference

Konference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Land/Område	Singapore
By	Singapore
Periode	18/09/2022 → 22/09/2022

Adgang til dokumentet

10.1007/978-3-031-09327-2_11

Citationsformater

Moshfeghifar, F., Nielsen, M. K., Tascon Vidarte, J. D., Darkner, S., & Erleben, K. (2022). A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility. I Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes (s. 155.169). Springer. https://doi.org/10.1007/978-3-031-09327-2_11

A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility. / Moshfeghifar, Faezeh; Nielsen, Max Kragballe; Tascon Vidarte, Jose David; Darkner, Sune ; Erleben, Kenny.

Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes. Springer, 2022. s. 155.169.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Moshfeghifar, F, Nielsen, MK, Tascon Vidarte, JD, Darkner, S & Erleben, K 2022, A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility. i Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes. Springer, s. 155.169, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/2022. https://doi.org/10.1007/978-3-031-09327-2_11

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