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
Article

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.

Key words

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

References

  1. 1.
    Saini S. Radiologic measurement of tumor size in clinical trials. AJR 176:333–334, 2001PubMedGoogle Scholar
  2. 2.
    Gavrielides MA, Kinnard LM, Myers KJ, et al. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 251:26–37, 2009CrossRefPubMedGoogle Scholar
  3. 3.
    Hopper KD, Kasales CJ, Van Slyke MA, et al. Analysis of interobserver and intraobserver variability in CT tumor measurements. AJR 167:851–854, 1996PubMedGoogle Scholar
  4. 4.
    Schwartz LH, Ginsberg MS, DeCorato D, et al. Evaluation of tumor measurement in oncology: use of film-based and electronic techniques. J Clin Oncol 18:2179–2184, 2000PubMedGoogle Scholar
  5. 5.
    Zijdenbos AP, Forghani R, Evans AC. Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21:1280–1291, 2002Google Scholar
  6. 6.
    Yu H, Caldwell C, Mah K, et al. Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys 75(2):618–625, 2009Google Scholar
  7. 7.
    Suit H. The Gray Lecture 2001: coming technical advances in radiation oncology. Int J Radiat Oncol Biol Phys 53(4):798–809, 2002CrossRefPubMedGoogle Scholar
  8. 8.
    De Xivry JO, Janssens G, Bosmans G, et al. Tumor delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients. Radiother Oncol 85:232–238, 2007CrossRefGoogle Scholar
  9. 9.
    Hamilton CS, Ebert MA. Volumetric uncertainty in radiotherapy. Clin Oncol 17:456–464, 2005CrossRefGoogle Scholar
  10. 10.
    Cao L, Li X, Zhan J, Chen W. Automated lung segmentation algorithm for CAD system of thoracic CT. Journal of Medical Colleges of PLA 23:215–222, 2008CrossRefGoogle Scholar
  11. 11.
    Lao Z, Shen D, Liu D, et al. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 15:300–313, 2008CrossRefPubMedGoogle Scholar
  12. 12.
    Kostopoulos S, Glotsos D, Kagadis GC, et al. A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA. Comput Graph 31:493–500, 2007CrossRefGoogle Scholar
  13. 13.
    Goodman LR, Gulsun M, Washington L, et al. Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR 186:989–994, 1996CrossRefGoogle Scholar
  14. 14.
    Alfano B, Brunetti A, Larabina M, et al. Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis. J Magn Reson Imaging 12:799–807, 2000CrossRefPubMedGoogle Scholar
  15. 15.
    Vannier MW, Pilgram TK, Speidel CM, et al. Validation of magnetic resonance imaging (MRI) multispectral tissue classification. Comput Med Imaging graph 15:217–223, 1991CrossRefPubMedGoogle Scholar
  16. 16.
    Zijdenbos AP, Dawant BM, Margolin RA. Measurement reliability and reproducibility in manual and semi-automatic MRI segmentation. Proceedings of the 15th IEEE-Engineering in Medicine and Biology Society 15:162–163, 1993CrossRefGoogle Scholar
  17. 17.
    Street E, Hadjiiski L, Sahiner B, et al. Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation. Med Phys 24:4399–4408, 2007CrossRefGoogle Scholar
  18. 18.
    Anbeek P, Vincken KL, van Bochove GS, et al. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27:795–804, 2005CrossRefPubMedGoogle Scholar
  19. 19.
    Solloway S, Hutchinson CE, Waterton JC, et al. The use of active shape models for making thickness measurements of articular cartilage from MR images. Magn Reson Med 37:943–952, 1997CrossRefPubMedGoogle Scholar
  20. 20.
    McWalter EJ, Wirth W, Siebert M, et al. Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images. Osteoarthr Cartil 13:48–53, 2005CrossRefPubMedGoogle Scholar
  21. 21.
    Kravits JF. Of mice and pen: effects of input device on different age groups performing goal-oriented tasks. Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting 2007: 45–49Google Scholar
  22. 22.
    MacKenzie IS, Sellen A, Buxton W. A comparison of input devices in elemental pointing and dragging tasks. Proceedings of ACM CHI’91 Conference on Human Factors in Computing Systems 161–166, 1991Google Scholar
  23. 23.
    Kotani K, Horii K. An analysis of muscular load and performance in using a pen tablet system. J Physiol Anthropol Appl Hum Sci 22:89–95, 2003CrossRefGoogle Scholar
  24. 24.
    Bertuca DJ. Letting go of the mouse: using alternative computer input devices to improve productivity and reduce injury. OCLC Syst Serv 17(2):79–83, 2001CrossRefGoogle Scholar
  25. 25.
    Larsson SN, Stapleton S, Larsson L. A comparison of speed and accuracy of contouring using mouse versus graphics tablet. Clin Oncol 19:S36, 2007Google Scholar

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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