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Published in: Journal of Digital Imaging 1/2011

01-02-2011

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

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2011

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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.
Literature
1.
go back to reference Saini S. Radiologic measurement of tumor size in clinical trials. AJR 176:333–334, 2001PubMed Saini S. Radiologic measurement of tumor size in clinical trials. AJR 176:333–334, 2001PubMed
2.
go back to reference Gavrielides MA, Kinnard LM, Myers KJ, et al. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 251:26–37, 2009CrossRefPubMed Gavrielides MA, Kinnard LM, Myers KJ, et al. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 251:26–37, 2009CrossRefPubMed
3.
go back to reference Hopper KD, Kasales CJ, Van Slyke MA, et al. Analysis of interobserver and intraobserver variability in CT tumor measurements. AJR 167:851–854, 1996PubMed Hopper KD, Kasales CJ, Van Slyke MA, et al. Analysis of interobserver and intraobserver variability in CT tumor measurements. AJR 167:851–854, 1996PubMed
4.
go back to reference 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, 2000PubMed 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, 2000PubMed
5.
go back to reference 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, 2002 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, 2002
6.
go back to reference 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, 2009 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, 2009
7.
go back to reference Suit H. The Gray Lecture 2001: coming technical advances in radiation oncology. Int J Radiat Oncol Biol Phys 53(4):798–809, 2002CrossRefPubMed Suit H. The Gray Lecture 2001: coming technical advances in radiation oncology. Int J Radiat Oncol Biol Phys 53(4):798–809, 2002CrossRefPubMed
8.
go back to reference 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, 2007CrossRef 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, 2007CrossRef
9.
go back to reference Hamilton CS, Ebert MA. Volumetric uncertainty in radiotherapy. Clin Oncol 17:456–464, 2005CrossRef Hamilton CS, Ebert MA. Volumetric uncertainty in radiotherapy. Clin Oncol 17:456–464, 2005CrossRef
10.
go back to reference 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, 2008CrossRef 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, 2008CrossRef
11.
go back to reference 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, 2008CrossRefPubMed 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, 2008CrossRefPubMed
12.
go back to reference 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, 2007CrossRef 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, 2007CrossRef
13.
go back to reference 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, 1996CrossRef 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, 1996CrossRef
14.
go back to reference 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, 2000CrossRefPubMed 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, 2000CrossRefPubMed
15.
go back to reference Vannier MW, Pilgram TK, Speidel CM, et al. Validation of magnetic resonance imaging (MRI) multispectral tissue classification. Comput Med Imaging graph 15:217–223, 1991CrossRefPubMed Vannier MW, Pilgram TK, Speidel CM, et al. Validation of magnetic resonance imaging (MRI) multispectral tissue classification. Comput Med Imaging graph 15:217–223, 1991CrossRefPubMed
16.
go back to reference 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, 1993CrossRef 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, 1993CrossRef
17.
go back to reference 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, 2007CrossRef 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, 2007CrossRef
18.
go back to reference Anbeek P, Vincken KL, van Bochove GS, et al. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27:795–804, 2005CrossRefPubMed Anbeek P, Vincken KL, van Bochove GS, et al. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27:795–804, 2005CrossRefPubMed
19.
go back to reference 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, 1997CrossRefPubMed 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, 1997CrossRefPubMed
20.
go back to reference 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, 2005CrossRefPubMed 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, 2005CrossRefPubMed
21.
go back to reference 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: 45–49 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: 45–49
22.
go back to reference 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, 1991 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, 1991
23.
go back to reference 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, 2003CrossRef 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, 2003CrossRef
24.
go back to reference 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, 2001CrossRef 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, 2001CrossRef
25.
go back to reference Larsson SN, Stapleton S, Larsson L. A comparison of speed and accuracy of contouring using mouse versus graphics tablet. Clin Oncol 19:S36, 2007 Larsson SN, Stapleton S, Larsson L. A comparison of speed and accuracy of contouring using mouse versus graphics tablet. Clin Oncol 19:S36, 2007
Metadata
Title
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
Publication date
01-02-2011
Publisher
Springer-Verlag
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2011
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-009-9258-9

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