Temporally Consistent Segmentation of Brain Tissue From Longitudinal MR Data

Abstract

In this paper, we propose a new 4D segmentation formulation, aiming to improve the temporal consistency of adults' brain tissue segmentation. In our method, tissue segmentation result is represented by the membership functions of the tissues, which are derived as a result of minimizing energy in a fuzzy C-means (FCM) framework. We first introduce a variational formulation with a temporal consistency constraint on the membership functions, then convert the constraint to a vector-valued function from which the membership functions are directly computed according to an analytically defined mapping. These vector-valued functions capture the bias field and intensity means of each tissue addressing the temporal variation in intensity inhomogeneities and the intensity means of the images. The effectiveness is demonstrated on the Baltimore Longitudinal Study of Aging (BLSA) benchmark dataset. Our method takes few parameters which are easy to tune and achieves 96.4% of TC factor for gray matter, 98.1% of TC factor for white matter, significantly superior to the compared methods on this research line.

Keywords

• Temporal consistency
• brain tissue segmentation
• longitudinal MR data