Name
Title | ||
|---|---|---|
Computer Input Devices: Neutral Party or Source of Significant Error in Manual Lesion Segmentation? |
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 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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10278-009-9258-9 | J. Digital Imaging |
Keywords | Field | DocType |
Image segmentation,user-computer interface,computer assisted detection,computer hardware,data collection,human computer interaction,evaluation research,segmentation | Computer vision,Graphics tablet,Lesion,Medical imaging,Computer science,Segmentation,Mean squared error,Image segmentation,Optical mouse,Artificial intelligence,Input device | Journal |
Volume | Issue | ISSN |
24 | 1 | 1618-727X |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Chen | 1 | 6 | 1.66 |
| F. Jacob Seagull | 2 | 149 | 19.68 |
| Paul Nagy | 3 | 61 | 13.65 |
| Paras Lakhani | 4 | 32 | 5.63 |
| Elias R. Melhem | 5 | 138 | 8.73 |
| Eliot Siegel | 6 | 302 | 80.13 |
| Nabile Safdar | 7 | 49 | 8.05 |