Geodesic Patch-Based Segmentation
Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.
KeywordsRight Ventricle Geodesic Distance Spatial Context Registration Error Label Propagation
- 6.Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint Classification-Regression Forests for Spatially Structured Multi-object Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 870–881. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 11.Rousseau, F., Habas, P., Studholme, C.: A Supervised Patch-Based Approach for Human Brain Labeling. IEEE TMI 30(10), 1852–1862 (2011)Google Scholar
- 14.Wang, Z., Wolz, R., Tong, T., Rueckert, D.: Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 93–103. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 15.Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE TMI 32(9), 1723–1730 (2013)Google Scholar