Human Supervisory Control Framework for Interactive Medical Image Segmentation
In this work, interactive segmentation is integrated with an active contour model, and segmentation is posed as a human-supervisory-control problem. User input is tightly coupled with an automatic segmentation algorithm leveraging the user’s high-level anatomical knowledge and the automated method’s speed. Real-time visualization enables the user to quickly identify and correct the result in a subdomain where the variational model’s statistical assumptions do not agree with his expert knowledge. Methods developed in this work are applied to magnetic resonance imaging (MRI) volumes as part of a population study of human skeletal development. Segmentation time is reduced by approximately five times over similarly accurate manual segmentation of large bone structures.
KeywordsSegmentation Result Active Contour User Input Ground Truth Segmentation Interactive Segmentation
This work was supported in part by grants from AFOSR, ARO, ONR, and MDA. This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics. Finally, this project was supported by grants from the National Center for Research Resources (P41-RR-013218) and the National Institute of Biomedical Imaging and Bioengineering (P41-EB-015902) of the National Institutes of Health.
- 2.Westbrook, C. (ed.): MRI at a glance. Wiley, Malden (2010)Google Scholar
- 3.Sheridan, T. (ed.): Telerobotics, automation, and human supervisory control. MIT Press, Cambridge (1992)Google Scholar
- 6.Karasev, P., Kolesov, I., Chudy, K., Muller, G., Xerogeanes, J., Tannenbaum, A.: Interactive MRI segmentation with controlled active vision. In: IEEE Conference on Decision and Control and European Control Conference. Orlando, December 2011Google Scholar
- 7.Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary region segmentation of objects in n-d images. In: ICCV. Vancouver, July 2001Google Scholar
- 10.Gao, Y., Tannenbaum, A., Kikinis, R.: Simultaneous multi-object segmentation using local robust statistics and contour interaction. In: MICCAI Medical Computer Vision, Workshop (2010)Google Scholar
- 12.Tsai, A., Yezzi, A., J., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W.E., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imag. 22(2), 137–154 (2003)Google Scholar
- 14.Top, A., Hamarneh, G., Abugharbieh, R.: Spotlight: automated confidence-based user guidance for increasing efficiency in interactive 3d image segmentation. In: MICCAI, 2010Google Scholar