An automated approach to extracting head and brain circumference from MRI datasets

Abstract

Head circumference is a fundamental biometric parameter for brain growth in both the clinical pediatric setting and in developmental neuroscience. However, the gold standard for obtaining head circumference by manual tape measurement is notoriously error-prone. Further, while it is known that the growth trajectories of head and brain differ over time, a systematic comparison of these two parameters as a function of age does not yet exist. We developed a new and automated algorithm for obtaining head and brain circumference from MRI data. The algorithm mimics manual head circumference measurement by placing a convex hull around axial slices which must intersect with predefined anatomical landmarks. Several differently-tilted iterations are run and results are combined. In addition to obtaining head circumference, the approach can also be applied to gray matter only, providing “brain circumference” (gray matter hull perimeter) at the same level as head circumference. To assess validity, we used T1-weighted 3D datasets (n = 153) with available, manually measured head circumference values (age range 0–226 months [0–18.8 years]). To assess test-retest reliability, a second dataset (n = 3 with 40 scans each) was used. When compared with the current gold standard (manual measure), high validity was demonstrated for the new approach, with no systematic bias. The algorithm also showed a very high reliability across multiple measurements. Developmental trajectories of both head and brain circumference were generated and compared. In summary, the algorithm represents a valid and reliable method for the automated determination of head as well as brain circumference. It offers an objective way to assess these parameters in retrospect and prospectively, and may shed light on specific clinical situations where they differ, such as in the presence of enlarged subarachnoid spaces.