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Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Background

Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before.

Purpose

The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery.

Methods

The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth.

Results

The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm.

Conclusions

We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.

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Funding

This study was funded by the Swiss National Science Foundation (Grant number 205321_163224).

Author information

Author notes

  1. Guoyan Zheng and Jérôme Schmid share the senior authorship

Authors and Affiliations

  1. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland

    Dimitrios Damopoulos & Guoyan Zheng

  2. Department of Orthopaedic Surgery and Traumatology, Inselspital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland

    Till Dominic Lerch, Florian Schmaranzer & Moritz Tannast

  3. School of Health Sciences - Geneva, HES-SO University of Applied Sciences and Arts Western Switzerland, Avenue de Champel 47, 1206, Geneva, Switzerland

    Christophe Chênes & Jérôme Schmid

Authors
  1. Dimitrios Damopoulos
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  2. Till Dominic Lerch
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  5. Christophe Chênes
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  7. Jérôme Schmid
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Correspondence to Dimitrios Damopoulos or Guoyan Zheng.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individuals included in the study.

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Damopoulos, D., Lerch, T.D., Schmaranzer, F. et al. Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration. Int J CARS 14, 545–561 (2019). https://doi.org/10.1007/s11548-018-1899-z

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  • DOI: https://doi.org/10.1007/s11548-018-1899-z

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