Automatic 3D segmentation of individual facial muscles using unlabeled prior information

Segmentation of facial soft tissues is required for surgical planning and evaluation, but this is laborious using manual methods and has been difficult to achieve with digital segmentation methods. A new automatic 3D segmentation method for facial soft tissues in magnetic resonance imaging (MRI) images was designed, implemented, and tested.

A region growing algorithm based on local energy functions, using intensity similarities among neighboring regions as criteria, was developed. This local energy function includes the neighborhood relationships not only in the same dataset but from other training datasets. This approach differs from previous studies where the prior information was represented as manual segmented atlases. In this study, a consensus of many datasets, none of which was labeled a priori, is used to guide the segmentation. The method was tested in MRI scans for adult facial structures. MRI scans were obtained from the Alzheimer's disease neuroimaging initiative database. Comparison was made to results from expert manual segmentation and region growing techniques.

The volumetric overlap between automated 3D segmentation results and the ground truth was 82.6% for masseter and 78.8% for temporalis tissues.

A new automated method to segment various facial soft tissues was implemented and the results compared with standard region growing results. The proposed method shows 3.9% improvement in accuracy over the standard method. Reduction in segmentation errors was consistently achieved in MRI scans.