Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers

Norio Fukuda, Yoshito Otake, Masaki Takao, Futoshi Yokota, Takeshi Ogawa, Keisuke Uemura, Ryota Nakaya, Kazunori Tamura, Robert B. Grupp, Amirhossein Farvardin, Mehran Armand, Nobuhiko Sugano, Yoshinobu Sato

Research output: Contribution to journalArticle

Abstract

Purpose: 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. Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)733-742
Number of pages10
JournalInternational journal of computer assisted radiology and surgery
Volume12
Issue number5
DOIs
StatePublished - May 1 2017

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Atlases
Cadaver
Muscle
Hip
Muscles
Bone
Bone and Bones
Dissection
Experiments
Orthopedics
Patient rehabilitation
Magnetic resonance imaging
Surgery
Medicine
Lower Extremity
Rehabilitation
Planning

Keywords

  • Biomechanical modeling
  • Hip joint
  • Muscle attachment
  • Statistical estimation

ASJC Scopus subject areas

  • Surgery
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

Cite this

Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers. / Fukuda, Norio; Otake, Yoshito; Takao, Masaki; Yokota, Futoshi; Ogawa, Takeshi; Uemura, Keisuke; Nakaya, Ryota; Tamura, Kazunori; Grupp, Robert B.; Farvardin, Amirhossein; Armand, Mehran; Sugano, Nobuhiko; Sato, Yoshinobu.

In: International journal of computer assisted radiology and surgery, Vol. 12, No. 5, 01.05.2017, p. 733-742.

Research output: Contribution to journalArticle

Fukuda, Norio ; Otake, Yoshito ; Takao, Masaki ; Yokota, Futoshi ; Ogawa, Takeshi ; Uemura, Keisuke ; Nakaya, Ryota ; Tamura, Kazunori ; Grupp, Robert B. ; Farvardin, Amirhossein ; Armand, Mehran ; Sugano, Nobuhiko ; Sato, Yoshinobu. / Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers. In: International journal of computer assisted radiology and surgery. 2017 ; Vol. 12, No. 5. pp. 733-742.
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abstract = "Purpose: 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. Conclusion: 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.",
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T1 - Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers

AU - Fukuda, Norio

AU - Otake, Yoshito

AU - Takao, Masaki

AU - Yokota, Futoshi

AU - Ogawa, Takeshi

AU - Uemura, Keisuke

AU - Nakaya, Ryota

AU - Tamura, Kazunori

AU - Grupp, Robert B.

AU - Farvardin, Amirhossein

AU - Armand, Mehran

AU - Sugano, Nobuhiko

AU - Sato, Yoshinobu

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N2 - Purpose: 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. Conclusion: 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.

AB - Purpose: 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. Conclusion: 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.

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KW - Muscle attachment

KW - Statistical estimation

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