Literature DB >> 20049624

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

James Y Chen1, F Jacob Seagull, Paul Nagy, Paras Lakhani, Elias R Melhem, Eliot L Siegel, Nabile M Safdar.   

Abstract

Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P < 0.0001). Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

Entities:  

Mesh:

Year:  2011        PMID: 20049624      PMCID: PMC3046792          DOI: 10.1007/s10278-009-9258-9

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  18 in total

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3.  Validation of magnetic resonance imaging (MRI) multispectral tissue classification.

Authors:  M W Vannier; T K Pilgram; C M Speidel; L R Neumann; D L Rickman; L D Schertz
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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

Review 6.  Volumetric uncertainty in radiotherapy.

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7.  Evaluation of tumor measurements in oncology: use of film-based and electronic techniques.

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8.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.

Authors:  Alex P Zijdenbos; Reza Forghani; Alan C Evans
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Review 9.  Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images.

Authors:  E J McWalter; W Wirth; M Siebert; R M O von Eisenhart-Rothe; M Hudelmaier; D R Wilson; F Eckstein
Journal:  Osteoarthritis Cartilage       Date:  2005-01       Impact factor: 6.576

10.  Analysis of interobserver and intraobserver variability in CT tumor measurements.

Authors:  K D Hopper; C J Kasales; M A Van Slyke; T A Schwartz; T R TenHave; J A Jozefiak
Journal:  AJR Am J Roentgenol       Date:  1996-10       Impact factor: 3.959

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  1 in total

1.  Stylus/tablet user input device for MRI heart wall segmentation: efficiency and ease of use.

Authors:  Bedros Taslakian; Antonio Pires; Dan Halpern; James S Babb; Leon Axel
Journal:  Eur Radiol       Date:  2018-05-02       Impact factor: 5.315

  1 in total

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