title: Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers.
abstract: OBJECTIVE
  Patient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments.
  
  
  METHODS
  Eight fresh cadaver specimens of the lower extremity were used in the experiments. Before dissection, CT images of all the specimens were acquired and the bone regions in CT images were extracted using an automated segmentation method to reconstruct the bone shape models. During dissection, ten different muscle attachment regions were recorded with an optical motion tracker. Then, these regions obtained from eight cadavers were integrated on an average bone surface via non-rigid registration, and muscle attachment probabilistic atlases (PAs) were constructed. An average muscle attachment region derived from the PA was non-rigidly mapped to the patients bone surface to estimate the patient-specific muscle attachment region.
  
  
  RESULTS
  Average Dice similarity coefficient between the true and estimated attachment areas computed by the proposed method was more than 10% higher than the one computed by a previous method in most cases and the average boundary distance error of the proposed method was 1.1 mm smaller than the previous method on average.
  
  
  CONCLUSIONS
  We conducted cadaver experiments to measure the attachment regions of the hip muscles and constructed PAs of the muscle attachment regions. The muscle attachment PA clarified the variations of the location of the muscle attachments and allowed us to estimate the patient-specific attachment area more accurately based on the patient bone shape derived from CT.
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan. n-fukuda@is.naist.jp.
authors.forename: Norio
authors.identifier: http://orcid.org/0000-0003-0332-8581
authors.initials: N
authors.lastname: Fukuda
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Otake
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Masaki
authors.initials: M
authors.lastname: Takao
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
authors.forename: Futoshi
authors.initials: F
authors.lastname: Yokota
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Ogawa
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Uemura
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Ryota
authors.initials: R
authors.lastname: Nakaya
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Kazunori
authors.initials: K
authors.lastname: Tamura
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Grupp
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Amirhossein
authors.initials: A
authors.lastname: Farvardin
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Mehran
authors.initials: M
authors.lastname: Armand
authors.affiliation: Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
authors.forename: Nobuhiko
authors.initials: N
authors.lastname: Sugano
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Sato
mesh_terms: D000368:Aged; D000465:Algorithms; D001696:Biomechanical Phenomena; D002102:Cadaver; D005260:Female; D006615:Hip / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009038:Motion; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Biomechanical modeling; Hip joint; Muscle attachment; Statistical estimation