Guided Structure-Aligned Segmentation of Volumetric Data
Segmentation of volumetric images is considered a time and resource intensive bottleneck in scientific endeavors. Automatic methods are becoming more reliable, but many data sets still require manual intervention. Key difficulties include navigating the 3D image, determining where to place marks, and maintaining consistency between marks and segmentations. Clinical practice often requires segmenting many different instances of a specific structure. In this research we leverage the similarity of a repeated segmentation task to address these difficulties and reduce the cognitive load for segmenting on non-traditional planes. We propose the idea of guided contouring protocols that provide guidance in the form of an automatic navigation path to arbitrary cross sections, example marks from similar data sets, and text instructions. We present a user study that shows the usability of this system with non-expert users in terms of segmentation accuracy, consistency, and efficiency.
The authors would like to thank Dr. Daniel Low, formerly of the Washington University School of Medicine in St. Louis for the liver data sets, Dr. Sandra Rugonyi of Oregon Health & Science University for the aorta data sets, and Dr. Philip Bayly of Washington University in St. Louis for the ferret brain data sets.
This work was supported by the National Science Foundation under grants DEB-1053554 and IIS-1302142.
- 5.Hamarneh, G., Yang, J., McIntosh, C., Langille, M.: 3d live-wire-based semi-automatic segmentation of medical images. SPIE Med. Imaging 5747, 1597–1603 (2005)Google Scholar
- 6.Heckel, F., Konrad, O., Hahn, H.K., Peitgen, H.O.: Interactive 3D medical image segmentation with energy-minimizing implicit functions. Comput. Graph. Spec. Issue Vis. Comput. Biol. Med. 35(2), 275–287 (2011)Google Scholar
- 7.Khan, A., Mordatch, I., Fitzmaurice, G., Matejka, J., Kurtenbach, G.: Viewcube: a 3d orientation indicator and controller. In: Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games, I3D 2008, pp. 17–25. ACM (2008)Google Scholar
- 8.Macedo, I., Gois, J., Velho, L.: Hermite interpolation of implicit surfaces with radial basis functions. In: 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), pp. 1–8, October 2009Google Scholar
- 10.Sowell, R., Liu, L., Ju, T., Grimm, C., Abraham, C., Gokhroo, G., Low, D.: VolumeViewer: an interactive tool for fitting surfaces to volume data. In: SBIM 2009: Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling, pp. 141–148. ACM (2009)Google Scholar
- 11.Sowell, R.T.: Modeling Surfaces from Volume Data Using Nonparallel Contours. Ph.D. thesis, Washington Univ. in St. Louis (2012)Google Scholar
- 12.Stoakley, R., Conway, M.J., Pausch, R.: Virtual reality on a wim: interactive worlds in miniature. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1995, pp. 265–272. ACM (1995)Google Scholar
- 13.Top, A., Hamarneh, G., Abugharbieh, R.: Active learning for interactive 3d image segmentation. In: Proceedings of the 14th International Conference on Medical Image Computing and Computer-assisted Intervention - Part III, pp. 603–610 (2011)Google Scholar
- 14.Top, A., Hamarneh, G., Abugharbieh, R.: Spotlight: automated confidence-based user guidance for increasing efficiency in interactive 3D image segmentation. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 204–213. Springer, Heidelberg (2011) Google Scholar
- 15.Weiss, E., Richter, S., Krauss, T., Metzelthin, S.I., Hille, A., Pradier, O., Siekmeyer, B., Vorwerk, H., Hess, C.F.: Conformal radiotherapy planning of cervix carcinoma: differences in the delineation of the clinical target volume. A comparison between gynaecologic and radiation oncologists. Radiother Oncol. 67(1), 87–95 (2003)CrossRefGoogle Scholar
- 16.Wirjadi, O.: Survey of 3D image segmentation methods. Technical report 123, Fraunhofer ITWM (2007)Google Scholar