Literature DB >> 15639637

Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images.

E J McWalter1, W Wirth, M Siebert, R M O von Eisenhart-Rothe, M Hudelmaier, D R Wilson, F Eckstein.   

Abstract

OBJECTIVE: To study the effect of new interactive computer input devices on cartilage segmentation in terms of time, consistency between input devices, and precision in quantitative magnetic resonance imaging (qMRI).
DESIGN: We compared two new input devices, an interactive digitizing tablet and an interactive touch-sensitive screen, to a traditional mouse. Medial tibial and patellar cartilage of six healthy and six osteoarthritic knees were segmented using each input device. Cartilage volume, surface area and mean thickness were assessed using a validated algorithm and used to determine consistency and precision. Segmentation time was also measured.
RESULTS: Segmenting with an interactive touch-sensitive screen reduced segmentation time by 15% when compared to the traditional mouse but we found no significant difference in segmentation time between the interactive digitizing tablet and the traditional mouse. We found no difference in consistency or precision of cartilage volume, mean thickness or surface area between the three input devices tested.
CONCLUSIONS: We conclude that measurements of cartilage made using articular cartilage segmentation from MR images are independent of the input device chosen for user interaction.

Entities:  

Mesh:

Year:  2005        PMID: 15639637     DOI: 10.1016/j.joca.2004.09.008

Source DB:  PubMed          Journal:  Osteoarthritis Cartilage        ISSN: 1063-4584            Impact factor:   6.576


  15 in total

1.  Semiautomated digital analysis of knee joint space width using MR images.

Authors:  Filippo Agnesi; Kimberly K Amrami; Carlo A Frigo; Kenton R Kaufman
Journal:  Skeletal Radiol       Date:  2007-01-23       Impact factor: 2.199

2.  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

3.  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

4.  Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

Authors:  Fang Liu; Zhaoye Zhou; Hyungseok Jang; Alexey Samsonov; Gengyan Zhao; Richard Kijowski
Journal:  Magn Reson Med       Date:  2017-07-21       Impact factor: 4.668

5.  Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.

Authors:  Romil F Shah; Alejandro M Martinez; Valentina Pedoia; Sharmila Majumdar; Thomas P Vail; Stefano A Bini
Journal:  J Arthroplasty       Date:  2019-07-24       Impact factor: 4.757

6.  Novel fast semi-automated software to segment cartilage for knee MR acquisitions.

Authors:  J Duryea; G Neumann; M H Brem; W Koh; F Noorbakhsh; R D Jackson; J Yu; C B Eaton; P Lang
Journal:  Osteoarthritis Cartilage       Date:  2006-12-22       Impact factor: 6.576

7.  Test-retest reliability of tibiofemoral joint space width measurements made using a low-dose standing CT scanner.

Authors:  Neil A Segal; John Bergin; Andrew Kern; Christian Findlay; Donald D Anderson
Journal:  Skeletal Radiol       Date:  2016-12-01       Impact factor: 2.199

8.  Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: analysis of longitudinal data from the Osteoarthritis Initiative (OAI).

Authors:  T Iranpour-Boroujeni; A Watanabe; R Bashtar; H Yoshioka; J Duryea
Journal:  Osteoarthritis Cartilage       Date:  2010-12-10       Impact factor: 6.576

9.  [Evaluation of cartilage defects in the knee: validity of clinical, magnetic-resonance-imaging and radiological findings compared with arthroscopy].

Authors:  G Spahn; R Wittig; E Kahl; H M Klinger; T Mückley; G O Hofmann
Journal:  Unfallchirurg       Date:  2007-05       Impact factor: 1.000

10.  Deep convolutional neural network for segmentation of knee joint anatomy.

Authors:  Zhaoye Zhou; Gengyan Zhao; Richard Kijowski; Fang Liu
Journal:  Magn Reson Med       Date:  2018-05-17       Impact factor: 4.668

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