Children's Neuroblastoma Segmentation Using Morphological Features

Shengyang Li, Xiaoyun Zhang, Xiaoxia Wang, Yu-Min Zhong, Xiaofen Yao, Ya Zhang, Yanfeng Wang

Abstract: Neuroblastoma (NB) is a common type of cancer in children that can develop in the neck, chest, or abdomen. It causes about 15% of cancer deaths in children. However, the automatic segmentation of NB on CT images has been addressed weakly, mostly because children’s CT images have much lower contrast than adults, especially those aged less than one year. Furthermore, neuroblastomas can develop in different body parts and are usually in variable size and irregular shape, which also add to the difficulties of NB segmentation. In view of these issues, we propose a morphological constrained end-to-end NB segmentation approach by taking the sizes and shapes of tumors in consideration for more accurate boundaries. The morphological features of neuroblastomas are predicted as an auxiliary task while performing segmentation and used as additional supervision for the segmentation prediction. We collect 248 CT scans from distinct patients with manually-annotated labels to establish a dataset for NB segmentation. Our method is evaluated on this dataset as well as the public Brats2018, and experimental results shows that the morphological constraints can improve the performance of medical image segmentation networks.