Active contour segmentation and its robust implementation using level sets
have been studied thoroughly in the medical image analysis literature. Despite
the availability of these powerful methods, clinical research still largely
relies on manual slice-by-slice outlining for anatomical structure
segmentation. To bridge the gap between methodological advances and clinical
routine, we developed ITK-SNAP: an open source application intended to make
level set segmentation easily accessible to a wide range of users with various
levels of mathematical expertise. We briefly describe this new tool and report
the results of a validation study in which ITK-SNAP was compared to manual
segmentation of the caudate in the context of an ongoing child neuroimaging
autism study.