Automatic brain segmentation and validation: Image-based versus atlas-based deformable models

Due to the complexity of the brain surface, there is at present no segmentation method that proves to work automatically and consistently on any 3-D magnetic resonance (MR) images of the head. There is a definite lack of validation studies related to automatic brain extraction. In this work we present an image-based automatic method for brain segmentation and use its results as an input to a deformable model method which we call image-based deformable model. Combining image-based methods with a deformable model can lead to a robust segmentation method without requiring registration of the image volumes into a standardized space, the automation of which remains challenging for pathological cases. We validate our segmentation results on 3-D MP-RAGE (Magnetization-Prepared Rapid Gradient-Echo) volumes for the image model prior- and post-deformation and compare it to an atlas model prior and post-deformation. Our validation is based on volume measurement comparison to manually segmented data. Our analysis shows that the improvement afforded by the deformable model methods are statistically significant, however there are no significant difference between the image-based and atlas-based deformable model methods.