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 for this work

Image Analysis, Computational Modelling and Geometry

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › 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.

Original language	English
Title of host publication	Computational Biomechanics for Medicine : Towards Translation and Better Patient Outcomes
Publisher	Springer
Publication date	2022
Pages	155.169
ISBN (Print)	978-3-031-09326-5
ISBN (Electronic)	978-3-031-09327-2
DOIs	https://doi.org/10.1007/978-3-031-09327-2_11
Publication status	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sep 2022 → 22 Sep 2022

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/2022 → 22/09/2022

Access to Document

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

Cite this

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. In Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes (pp. 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. p. 155.169.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › 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. in Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes. Springer, pp. 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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