Landmarking and segmentation of computed tomographic images of pediatric patients with neuroblastoma

Abstract

Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis, treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors is difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and volume. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, we explore methods for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods are proposed to identify and segment automatically peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface. The results of segmentation of the vertebral column, the spinal canal, the diaphragm and the pelvic girdle are quantitatively evaluated by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs assisted in limiting the scope of the tumor segmentation process to the abdomen, and resulted in the reduction of the false-positive error rates by 22.4%, on the average, over ten CT exams of four patients, and improved the result of segmentation of neuroblastic tumors.