BET - Brain/Non-Brain Segmentation

There are many applications related to brain imaging which either require, or benefit from, the ability to accurately segment brain from non-brain tissue. For example, in the registration of functional images to high resolution MR images, both FMRI and PET functional images often contain little non-brain tissue because of the nature of the imaging, whereas the high resolution MR image probably will contain a considerable amount - eyes, skin, fat, muscle, etc - and thus registration robustness is improved if these non-brain parts of the image can be automatically removed before registration. Also, many tissue-type segmentation approaches (such as FAST, see below) require brain/non-brain segmentation to have been carried out before being they can function well.

We have developed a tool for fully automated brain extraction which runs robustly on a variety of MR modalities (tested on T1-weighted, T2-weighted, proton density, EPI, etc.), called BET (Brain Extraction Tool) [30]. At the core of the algorithm a triangular tesselation of a spherical surface is initialised inside the brain, and allowed to slowly deform, one vertex at a time, following forces that keep the surface well-spaced and smooth, whilst attempting to move towards the brain's edge (defined in terms of the local intensity structure, to reduce the effects of image bias field). If a suitably clean solution is not arrived at then the whole process is re-run with a higher smoothness constraint. Finally, if required, the outer surface of the skull can be estimated. See fig. 8 for example BET output.

Figure 8: Example BET and FAST output. Middle: extracted brain and external skull surface points from BET. Right: CSF, white and grey partial volume colour overlays from FAST.