Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from
CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is
planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method
to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary
planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional
region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm
based on normalized mutual information. The transformed mesh models actively located skull boundaries by
minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The
KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa
index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using
In this paper, we have developed a digital atlas of the pediatric human brain. Human brain atlases, used to visualize spatially complex structures of the brain, are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1 weighted MR data set of a 9 year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. Higher curvature surfaces were simplified with more reference points; lower curvature surfaces, with fewer. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/BrainAtlas) can be used to plan treatment, to conduct knowledge and modeldriven segmentation, and to analyze the shapes of brain structures in pediatric patients.
In this study, an automated method has been developed to identify the cerebellum from T1-weighted MR brain images of patients with medulloblastoma. A new objective function that is similar to Gibbs free energy in classic physics was defined; and the brain structure delineation was viewed as a process of minimizing Gibbs free energy. We used a rigid-body registration and an active contour (snake) method to minimize the Gibbs free energy in this study. The method was applied to 20 patient data sets to generate cerebellum images and volumetric results. The generated cerebellum images were compared with two manually drawn results. Strong correlations were found between the automatically and manually generated volumetric results, the correlation coefficients with each of manual results were 0.971 and 0.974, respectively. The average Jaccard similarities with each of two manual results were 0.89 and 0.88, respectively. The average Kappa indexes with each of two manual results were 0.94 and 0.93, respectively. These results showed this method was both robust and accurate for cerebellum segmentation. The method may be applied to various research and clinical investigation in which cerebellum segmentation and quantitative MR measurement of cerebellum are needed.
Due to the inherent risk of central nervous system (CNS) dissemination, children treated for either acute lymphoblastic leukemia (ALL) or malignant brain tumors (BT) receive aggressive CNS therapy. The primary objective of this study was to confirm a previously observed association between reduced volumes of normal-appearing white matter (NAWM) and intellectual and attentional deficits in survivors. A combined MR imaging set consisting of T1, T2, and PD images were collected for 221 children (110 BT; 112 ALL). MR imaging sets were segmented with a hybrid neural network algorithm and volumetric measurements were calculated for five slices centered on the basal ganglia. Summary measures of Overall Index, Omissions, d’ (attentiveness), and beta (risk-taking) were derived from the computer-administered Conners’ Continuous Performance Test (CPT). Age-corrected estimates of Full-Scale IQ (FSIQ) were also obtained. Pearson correlation analyses were performed between each neurocognitive measure and the volume of NAWM. The correlation between FSIQ and NAWM failed to reach statistical significance for the BT group but was highly significant for the more homogeneous ALL group. Larger Omission rates, decreased attentiveness and more risk taking were significantly associated with lower NAWM volumes in both groups of survivors. Long-term survivors are at increased risk for cognitive delays or deficits, which oftentimes impair future academic performance, employment, and quality of life. These long-term adverse effects of treatment appear to be due to a diminished ability to acquire new information and may be secondary to deficits in attention, which is thought to be supported by interhemispheric and intrahemispheric white matter tracts.