Literature DB >> 25051918

Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas.

Pooneh R Tabrizi1, Reza A Zoroofi, Futoshi Yokota, Satoru Tamura, Takashi Nishii, Yoshinobu Sato.   

Abstract

PURPOSE: Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested.
METHODS: A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans.
RESULTS: The semiautomatic anatomical-statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage.
CONCLUSIONS: The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.

Entities:  

Mesh:

Year:  2014        PMID: 25051918     DOI: 10.1007/s11548-014-1101-1

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  18 in total

1.  Nonrigid registration using free-form deformations: application to breast MR images.

Authors:  D Rueckert; L I Sonoda; C Hayes; D L Hill; M O Leach; D J Hawkes
Journal:  IEEE Trans Med Imaging       Date:  1999-08       Impact factor: 10.048

2.  Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure.

Authors:  Futoshi Yokota; Toshiyuki Okada; Masaki Takao; Nobuhiko Sugano; Yukio Tada; Yoshinobu Sato
Journal:  Med Image Comput Comput Assist Interv       Date:  2009

3.  LOGISMOS--layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint.

Authors:  Yin Yin; Xiangmin Zhang; Rachel Williams; Xiaodong Wu; Donald D Anderson; Milan Sonka
Journal:  IEEE Trans Med Imaging       Date:  2010-07-19       Impact factor: 10.048

4.  Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.

Authors:  Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

5.  The use of active shape models for making thickness measurements of articular cartilage from MR images.

Authors:  S Solloway; C E Hutchinson; J C Waterton; C J Taylor
Journal:  Magn Reson Med       Date:  1997-06       Impact factor: 4.668

6.  Anatomically corresponded regional analysis of cartilage in asymptomatic and osteoarthritic knees by statistical shape modelling of the bone.

Authors:  Tomos G Williams; Andrew P Holmes; John C Waterton; Rose A Maciewicz; Charles E Hutchinson; Robert J Moots; Anthony F P Nash; Chris J Taylor
Journal:  IEEE Trans Med Imaging       Date:  2010-04-08       Impact factor: 10.048

7.  Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model.

Authors:  Claude Kauffmann; Pierre Gravel; Benoît Godbout; Alain Gravel; Gilles Beaudoin; Jean-Pierre Raynauld; Johanne Martel-Pelletier; Jean-Pierre Pelletier; Jacques A de Guise
Journal:  IEEE Trans Biomed Eng       Date:  2003-08       Impact factor: 4.538

8.  Hip cartilage thickness measurement accuracy improvement.

Authors:  Yuanzhi Cheng; Shuguo Wang; Takaharu Yamazaki; Jie Zhao; Yoshikazu Nakajima; Shinichi Tamura
Journal:  Comput Med Imaging Graph       Date:  2007-09-29       Impact factor: 4.790

9.  Primal/dual linear programming and statistical atlases for cartilage segmentation.

Authors:  Ben Glocker; Nikos Komodakis; Nikos Paragios; Christian Glaser; Georgios Tziritas; Nassir Navab
Journal:  Med Image Comput Comput Assist Interv       Date:  2007

10.  Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee.

Authors:  Jurgen Fripp; Stuart Crozier; Simon K Warfield; Sébastien Ourselin
Journal:  IEEE Trans Med Imaging       Date:  2009-06-10       Impact factor: 10.048

View more
  2 in total

1.  Shape-based acetabular cartilage segmentation: application to CT and MRI datasets.

Authors:  Pooneh R Tabrizi; Reza A Zoroofi; Futoshi Yokota; Takashi Nishii; Yoshinobu Sato
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-10-20       Impact factor: 2.924

2.  Reliability of computer-assisted periacetabular osteotomy using a minimally invasive approach.

Authors:  Sepp De Raedt; Inger Mechlenburg; Maiken Stilling; Lone Rømer; Ryan J Murphy; Mehran Armand; Jyri Lepistö; Marleen de Bruijne; Kjeld Søballe
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-06-06       Impact factor: 2.924

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.