Segmentation and analysis of neuroblastoma

Segmentation of the tumor in computed tomographic (CT) images of neuroblastoma is a difficult problem due to its heterogeneity. In this paper, we present a survey of the nature, diagnosis, and management of the disease, and explore the potential application of image processing methods for the segmentation and analysis of the tumor. In particular, we study the use of opening-by-reconstruction and fuzzy connectivity to guide the segmentation process. We also investigate various strategies to improve the resulting tumor definition, including prior delineation of peripheral artifacts, peripheral fat, peripheral muscle, and the spinal canal, as well as postprocessing by morphological methods and deformable contours. Results of application of four procedures to 10 CT exams of four patients are analyzed. It is shown that opening-by-reconstruction is faster (by a factor of about 19) and provides a higher true-positive rate (83% versus 78%) than fuzzy connectivity. Leakage of the result of segmentation into neighboring structures remains to be a problem in cases where the tumor is significantly heterogeneous. Regardless, image processing methods could play an important role in improved management of neuroblastoma.