Literature DB >> 9178247

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

S Solloway1, C E Hutchinson, J C Waterton, C J Taylor.   

Abstract

Previously reported studied to quantify articular cartilage have used labor-intensive manual or semi-automatic data-driven techniques, demonstrating high accuracy and precision. However, none has been able to automate the segmentation process. This paper describes a fast, automatic, model-based approach to segmentation and thickness measurement of the femoral cartilage in 3D T1-weighted images using active shape models (ASMs). Systematic experiments were performed to assess the accuracy and precision of the technique with in vivo images of both normal and abnormal knees. Segmentation accuracy was determined by comparing the results of the segmentation with the boundaries delineated by a radiologist. The mean error in locating the boundary was 0.57 pixels. To assess the precision of the measurement technique, the mean thickness of the femoral cartilage was calculated for repeated scans of five healthy volunteers. A mean coefficient of variation (CV) of 2.8% was obtained for the thickness measurements.

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Year:  1997        PMID: 9178247     DOI: 10.1002/mrm.1910370620

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  25 in total

1.  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

Review 2.  Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Authors:  Valentina Pedoia; Sharmila Majumdar; Thomas M Link
Journal:  MAGMA       Date:  2016-02-25       Impact factor: 2.310

3.  Computer input devices: neutral party or source of significant error in manual lesion segmentation?

Authors:  James Y Chen; F Jacob Seagull; Paul Nagy; Paras Lakhani; Elias R Melhem; Eliot L Siegel; Nabile M Safdar
Journal:  J Digit Imaging       Date:  2011-02       Impact factor: 4.056

4.  Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method.

Authors:  K T Bae; H Shim; C Tao; S Chang; J H Wang; R Boudreau; C K Kwoh
Journal:  Osteoarthritis Cartilage       Date:  2009-06-23       Impact factor: 6.576

5.  Magnetic resonance imaging-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: data from the osteoarthritis initiative.

Authors:  Tuhina Neogi; Michael A Bowes; Jingbo Niu; Kevin M De Souza; Graham R Vincent; Joyce Goggins; Yuqing Zhang; David T Felson
Journal:  Arthritis Rheum       Date:  2013-08

6.  Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative.

Authors:  Erik B Dam; Martin Lillholm; Joselene Marques; Mads Nielsen
Journal:  J Med Imaging (Bellingham)       Date:  2015-04-20

7.  Accuracy of 3D cartilage models generated from MR images is dependent on cartilage thickness: laser scanner based validation of in vivo cartilage.

Authors:  Seungbum Koo; Nicholas J Giori; Garry E Gold; Chris O Dyrby; Thomas P Andriacchi
Journal:  J Biomech Eng       Date:  2009-12       Impact factor: 2.097

8.  Knee cartilage segmentation and thickness computation from ultrasound images.

Authors:  Amir Faisal; Siew-Cheok Ng; Siew-Li Goh; Khin Wee Lai
Journal:  Med Biol Eng Comput       Date:  2017-08-29       Impact factor: 2.602

9.  Quantitative susceptibility mapping of articular cartilage in patients with osteoarthritis at 3T.

Authors:  Hongjiang Wei; Huimin Lin; Le Qin; Steven Cao; Yuyao Zhang; Naying He; Weibo Chen; Fuhua Yan; Chunlei Liu
Journal:  J Magn Reson Imaging       Date:  2018-12-24       Impact factor: 4.813

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

Authors:  Pooneh R Tabrizi; Reza A Zoroofi; Futoshi Yokota; Satoru Tamura; Takashi Nishii; Yoshinobu Sato
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-07-23       Impact factor: 2.924

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