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

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.

[1]  K. Hopper,et al.  Analysis of interobserver and intraobserver variability in CT tumor measurements. , 1996, AJR. American journal of roentgenology.

[2]  Kyle J Myers,et al.  Noncalcified lung nodules: volumetric assessment with thoracic CT. , 2009, Radiology.

[3]  Benoit M. Dawant,et al.  Measurement reliability and reproducibility in manual and semi-automatic MRI segmentation , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[4]  F Eckstein,et al.  Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images. , 2005, Osteoarthritis and cartilage.

[5]  M. L. R. D. Christenson,et al.  Inherent Variability of CT Lung Nodule Measurements In Vivo Using Semiautomated Volumetric Measurements , 2007 .

[6]  Zhan Jie,et al.  Automated lung segmentation algorithm for CAD system of thoracic CT , 2008 .

[7]  Christos Davatzikos,et al.  Computer-assisted Segmentation of White Matter Lesions in 3d Mr Images Using Support Vector Machine 1 , 2022 .

[8]  Bruno Alfano,et al.  Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis , 2000 .

[9]  James F. Kravitz Of Mice and Pen: Effects of Input Device on Different Age Groups Performing Goal-Oriented Tasks , 2007 .

[10]  H. Suit,et al.  The Gray Lecture 2001: coming technical advances in radiation oncology. , 2002, International journal of radiation oncology, biology, physics.

[11]  S Saini,et al.  Radiologic measurement of tumor size in clinical trials: past, present, and future. , 2001, AJR. American journal of roentgenology.

[12]  Thomas K. Pilgram,et al.  Validation of magnetic resonance imaging (MRI) multispectral tissue classification. , 1991, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[13]  Lubomir M. Hadjiiski,et al.  Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation. , 2007, Medical physics.

[14]  Kentaro Kotani,et al.  An analysis of muscular load and performance in using a pen-tablet system. , 2003, Journal of physiological anthropology and applied human science.

[15]  L. Larsson,et al.  A Comparison of Speed and Accuracy of Contouring using Mouse versus Graphics Tablet , 2007 .

[16]  I. Poon,et al.  Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. , 2009, International journal of radiation oncology, biology, physics.

[17]  Abigail Sellen,et al.  A comparison of input devices in element pointing and dragging tasks , 1991, CHI.

[18]  C J Taylor,et al.  The use of active shape models for making thickness measurements of articular cartilage from MR images , 1997, Magnetic resonance in medicine.

[19]  David J. Bertuca Letting go of the mouse: using alternative computer input devices to improve productivity and reduce injury , 2001, OCLC Syst. Serv..

[20]  L. Schwartz,et al.  Evaluation of tumor measurements in oncology: use of film-based and electronic techniques. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[22]  Koen L. Vincken,et al.  Probabilistic segmentation of brain tissue in MR imaging , 2005, NeuroImage.

[23]  S. Mackenzie,et al.  A comparison of input device in elemental pointing and dragging task , 1991, CHI 1991.

[24]  Mathieu De Craene,et al.  Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients. , 2007, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  Panagiota Spyridonos,et al.  A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA , 2007, Comput. Graph..

[26]  M A Ebert,et al.  Volumetric uncertainty in radiotherapy. , 2005, Clinical oncology (Royal College of Radiologists (Great Britain)).