Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Zheng, Guoyan; Dong, Xiao; Rajamani, Kumar Thirunellai; Zhang, Xuan; Styner, M; Thoranghatte, Ramesh; Nolte, Lutz-Peter; Gonzalez Ballester, Miguel Angel (2007). Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation. IEEE transactions on biomedical engineering, 54(12), pp. 2109-2122. New York, N.Y.: Institute of Electrical and Electronics Engineers IEEE 10.1109/TBME.2007.895736

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Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Zheng, Guoyan; Dong, Xiao; Rajamani, Kumar Thirunellai; Zhang, Xuan; Styner, Martin Andreas; Nolte, Lutz-Peter and Gonzalez Ballester, Miguel Angel

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0018-9294

ISBN:

2109-2122

Publisher:

Institute of Electrical and Electronics Engineers IEEE

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:57

Last Modified:

07 Feb 2019 14:19

Publisher DOI:

10.1109/TBME.2007.895736

Web of Science ID:

000251226200001

BORIS DOI:

10.7892/boris.24173

URI:

https://boris.unibe.ch/id/eprint/24173 (FactScience: 47396)

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