A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma

Purpose: To develop an automated method for identification of the cerebella on magnetic resonance (MR) images of patients with medulloblastoma. Materials and Methods: The method used a template constructed from 10 patients' aligned MR head images, and the contour of this template was superimposed on the aligned data set of a given patient as the starting contour. The starting contour was then actively adjusted to locate the boundary of the cerebellum of the given patient. Morphologic operations were applied to the outlined volume to generate cerebellum images. The method was then applied to data sets of 20 other patients to generate cerebellum images and volumetric results. Results: Comparison of the automatically generated cerebellum images with two sets of manually traced images showed a strong correlation between the automatically and manually generated volumetric results (correlation coefficient, 0.97). The average Jaccard similarities were 0.89 and 0.88 in comparison to each of two manually traced images, respectively. The same comparisons yielded average kappa indexes of 0.94 and 0.93, respectively. Conclusion: The method was robust and accurate for cerebellum segmentation on MR images of patients with medulloblastoma. The method may be applied to investigations that require segmentation and quantitative measurement of MR images of the cerebellum.