Standard
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
Harvard
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
APA
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
Vancouver
Moshfeghifar F, Nielsen MK, Tascon Vidarte JD
, Darkner S, Erleben K.
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. 2022. p. 155.169
https://doi.org/10.1007/978-3-031-09327-2_11
Author
Moshfeghifar, Faezeh ; Nielsen, Max Kragballe ; Tascon Vidarte, Jose David ; Darkner, Sune ; Erleben, Kenny. / A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility. Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes. Springer, 2022. pp. 155.169
Bibtex
@inproceedings{322be6b852f44ed2a71b1ca16e2f5709,
title = "A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility",
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.",
author = "Faezeh Moshfeghifar and Nielsen, {Max Kragballe} and {Tascon Vidarte}, {Jose David} and Sune Darkner and Kenny Erleben",
year = "2022",
doi = "10.1007/978-3-031-09327-2_11",
language = "English",
isbn = "978-3-031-09326-5",
pages = "155.169",
booktitle = "Computational Biomechanics for Medicine",
publisher = "Springer",
address = "Switzerland",
note = "25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
}
RIS
TY - GEN
T1 - A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility
AU - Moshfeghifar, Faezeh
AU - Nielsen, Max Kragballe
AU - Tascon Vidarte, Jose David
AU - Darkner, Sune
AU - Erleben, Kenny
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-031-09327-2_11
DO - 10.1007/978-3-031-09327-2_11
M3 - Article in proceedings
SN - 978-3-031-09326-5
SP - 155.169
BT - Computational Biomechanics for Medicine
PB - Springer
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -
