Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 7/2018

06-04-2018 | Original Article

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

Authors: Futoshi Yokota, Yoshito Otake, Masaki Takao, Takeshi Ogawa, Toshiyuki Okada, Nobuhiko Sugano, Yoshinobu Sato

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 7/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN
next article Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Andrews S, Hamarneh G (2015) The generalized log-ratio transformation: learning shape and adjacency priors for simultaneous thigh muscle segmentation. IEEE Trans Med Imaging 34(9):1773–1787CrossRefPubMed
2.
Arthofer C, Morgan PS, Pitiot A (2016) Hierarchical multi-atlas segmentation using label-specific embeddings, target-specific templates and patch refinement. In: Patch-based techniques in medical imaging LNCS 9993, pp 84–91
3.
Baudin PY, Azzabou N, Carlier PG, Paragios N (2012) Prior knowledge, random walks and human skeletal muscle segmentation. In: International conference on medical image computing and computer-assisted intervention: MICCAI 2012, vol 15(Pt 1), pp 569–576
4.
Diaz-Boladeras M, Angulo C, Domènech M, Albo-Canals J, Serrallonga N, Raya C, Barco A (2016) XIV Mediterranean conference on medical and biological engineering and computing 2016: MEDICON 2016. In: Conference on Medical and Biological Engineering and Computing, pp. 1179–1184
5.
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRef
6.
Fukuda N, Otake Y, Takao M, Yokota F, Ogawa T, Nakaya R, Tamura K, Grupp R, Farvardin A, Sugano N, Sato Y (2016) Statistical estimation of attachment of hip muscles based on measurement in cadavers. In: 16th annual meeting of CAOS-international proceedings, pp. 351–354
7.
Gilles B, Magnenat-Thalmann N (2010) Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med Image Anal 14(3):291–302CrossRefPubMed
8.
9.
Glocker B, Sotiras A, Komodakis N, Paragios N (2011) Deformable medical image registration: setting the state of the art with discrete methods. Ann Rev Biomed Eng 13:219–244CrossRef
10.
Guess TM, Stylianou AP, Kia M (2014) Concurrent prediction of muscle and tibiofemoral contact forces during treadmill gait. J Biomech Eng 136(2):021–032CrossRef
11.
Huo Y, Plassard AJ, Carass A, Resnick SM, Pham DL, Prince JL, Landman BA (2016) Consistent cortical reconstruction and multi-atlas brain segmentation. NeuroImage 138:197–210CrossRefPubMedPubMedCentral
12.
Isgum I, Staring M, Rutten A, Prokop M, Viergever Ma, van Ginneken B (2009) Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000–10CrossRefPubMed
13.
Kamiya N, Muramatsu C, Zhou X, Chen H, Yokoyama R, Hara T (2013) Model-based approach to recognize the rectus abdominis muscle in CT by use of a virtual image-unfolding technique. IEICE Trans Inf Syst E–96–D(4):2–3
14.
Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O (2014) Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging 41(6):1558–1569CrossRefPubMed
15.
Kohout J, Clapworthy GJ, Zhao Y, Tao Y, Gonzalez-Garcia G, Dong F, Wei H, Kohoutová E (2013) Patient-specific fibre-based models of muscle wrapping. Interface Focus 3(2):20120,062CrossRef
16.
Le Troter A, Fouré A, Guye M, Confort-Gouny S, Mattei JP, Gondin J, Salort-Campana E, Bendahan D (2016) Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. Magn Reson Mater Phys Biol Med 29(2):245–257CrossRef
17.
Ledig C, Heckemann RA, Hammers A, Lopez JC, Newcombe VF, Makropoulos A, Lötjönen J, Menon DK, Rueckert D (2015) Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 21(1):40–58CrossRefPubMed
18.
Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, Do S (2017) Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging 30(4):487–498CrossRefPubMedPubMedCentral
19.
Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape location and unsupervised intensity priors. Med Image Anal 26(1):1–18CrossRefPubMedPubMedCentral
20.
Ou Y, Doshi J (2012) Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. MICCAI Grand Chall Prostate MR Image Segmen 7:1–7
21.
Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M (2016) Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging 35(2):512–520CrossRefPubMed
22.
Rasch A, Bystrom AH, Dalen N, Martinez-Carranza N, Berg HE (2009) Persisting muscle atrophy two years after replacement of the hip. J Bone Joint Surg Br 91–B(5):583–588CrossRef
23.
24.
Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–21CrossRefPubMed
25.
Styner M, Lee J, Chin B, Chin M (2008) 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation. Midas J 1–6
26.
Takao M, Ogawa T, Yokota F, Otake Y, Hamada H, Sakai T, Sato Y, Sugano N (2017) Pre-operative fatty degeneration of gluteus minimus predicts falls after tha. Bone Joint J 99(SUPP 6):39–39
27.
Thelen DG, Won Choi K, Schmitz AM (2014) Co-simulation of neuromuscular dynamics and knee mechanics during human walking. J Biomech Eng 136(2):021,033CrossRef
28.
Uemura K, Takao M, Sakai T, Nishii T, Sugano N (2016) Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplasty 31(4):906–912.e1CrossRefPubMed
29.
Webb JD, Blemker SS, Delp SL (2014) 3D finite element models of shoulder muscles for computing lines of actions and moment arms. Comput Methods Biomech Biomed Eng 17(8):829–37CrossRef
30.
Wu D, Ma T, Ceritoglu C, Li Y, Chotiyanonta J, Hou Z, Hsu J, Xu X, Brown T, Miller MI, Mori S (2016) Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. NeuroImage 125:120–130CrossRefPubMed
31.
Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. In: International conference on medical image computing and computer-assisted intervention: MICCAI 2013, pp 190–197
32.
Yokota F, Takaya M, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2012) Automated muscle segmentation from 3D CT data of the hip using hierarchical multi-atlas method. In: The 12th annual meeting of the international society for computer assisted orthopaedic surgery: CAOS 2012, vol 1, pp 16–18
Metadata
Title
Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method
Authors
Futoshi Yokota
Yoshito Otake
Masaki Takao
Takeshi Ogawa
Toshiyuki Okada
Nobuhiko Sugano
Yoshinobu Sato
Publication date
06-04-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 7/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1758-y

Other articles of this Issue 7/2018

International Journal of Computer Assisted Radiology and Surgery 7/2018 Go to the issue

Original Article

Force estimation from OCT volumes using 3D CNNs

Original Article

Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated

Original Article

Multistage segmentation model and SVM-ensemble for precise lung nodule detection

Original Article

Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

Original Article

Laparoscopic ultrasound manipulator with a spring-based elastic mechanism

Original Article

Multi-ray medical ultrasound simulation without explicit speckle modelling

Premium Partner

    Image Credits