Journal of Digital Imaging

, Volume 24, Issue 1, pp 135–141 | Cite as

Computer Input Devices: Neutral Party or Source of Significant Error in Manual Lesion Segmentation?

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


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.

Key words

Image segmentation user-computer interface computer assisted detection computer hardware data collection human computer interaction evaluation research segmentation 


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Copyright information

© Society for Imaging Informatics in Medicine 2009

Authors and Affiliations

  • James Y. Chen
    • 1
    • 2
    • 3
  • F. Jacob Seagull
    • 2
  • Paul Nagy
    • 2
  • Paras Lakhani
    • 1
    • 2
  • Elias R. Melhem
    • 1
  • Eliot L. Siegel
    • 2
    • 4
  • Nabile M. Safdar
    • 2
  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.University of MarylandCollege ParkUSA
  3. 3.San Diego Veterans Administration HospitalUniversity of California San DiegoSan DiegoUSA
  4. 4.Baltimore Veterans Administration HospitalBaltimoreUSA

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