Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

  • Futoshi Yokota
  • Yoshito Otake
  • Masaki Takao
  • Takeshi Ogawa
  • Toshiyuki Okada
  • Nobuhiko Sugano
  • Yoshinobu Sato
Original Article
  • 59 Downloads

Abstract

Purpose

Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.

Method

We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.

Results

The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).

Conclusion

We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.

Keywords

Multi-atlas label fusion Musculoskeletal segmentation Hierarchical strategy 

Notes

Acknowledgements

This research was supported by MEXT/JSPS KAKENHI 26108004, 25242051 and 16K01411, JST PRESTO 20407, and AMED/ETH the strategic Japanese-Swiss cooperative research program.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Human and animals rights

This study has been approved by the Institutional Review Board of Osaka University Hospital (No. 15056), where the patients’ medical data used in this work were obtained.

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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Graduate School of Information ScienceNara Institute of Science and TechnologyIkomaJapan
  2. 2.Graduate School of MedicineOsaka UniversitySuitaJapan
  3. 3.Faculty of MedicineUniversity of TsukubaTsukubaJapan

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