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The algorithm is essentially the same as that described in the 2005 paper, but with a few changes that make it more robust and hopefully more accurate. 1) The tissue probability maps include a few new tissues (bone, soft tissue and air), so should encode a better model of the head. These tissue probability maps have also been smoothed in a slightly different way to ensure that there are no zeros anywhere and that the logarithms of the values are reasonably smooth. Some differences will arise from the different set of tissue probabilities. The extra tissues in the model give a number of advantages: a) The initial affine registration is more robust because it expects bone and scalp outside the brain. The older implementation didn't have this information, so the initial affine registration often caused problems. b) By knowing about the existence of bone, the model is better able to separate it from CSF (better estimates of TIV). With a more accurate idea about CSF, it should also be able to separate GM from CSF more accurately. Similarly, it expects there to be some soft tissue close to the brain, which may have intensities similar to GM. This may help to eliminate some of it being mis-classified as GM. c) Sometimes it is helpful to identify other tissue types. For example, knowledge of scalp surface and bone may be useful for M/EEG source localisation. Another aim was to have a better chance of separating tissue from air in the head (eg sinuses), which we hope could lead to improvements in EPI distortion correction. d) The algorithm now has more chance of identifying CSF from high quality CT images. This does not work so well for CT with thick slices, because the CSF around the brain has its intensity dominated by partial volume between soft tissue and bone. However, it may work for data with thinner slices. 2) The deformation model is now more flexible than the old one. Previously, only about 1000 parameters were used to model the shape of the head, which is no-where near enough. Some of the technology that went into Dartel has been used as a framework for much more detailed deformation modelling (typically with about 700,000 parameters). 3) The way that the mixing proportions are used has been changed slightly. This may bias some aspects of the segmentation more towards the information in the template - which may be a good or bad thing. Mostly good I hope. 4) There is now the possibility of modelling multi-spectral images, rather than being limited to images of a single modality. 5) For single modality images, there is the option to use a non-parametric (histogram) representation of the intensity distributions of the different classes. This avoids some of the local optima that a mixture of Gaussians model can fall into. The non-parametric option can be used for multi-channel data, but it does not work well because the histograms are only 1D. Outer products of 1D histograms are used for representing multi-spectral intensity distributions - which is not an especially good approximation. 6) The strategy used by the initial affine registration is now closer to that used by the registration component of the main segmentation routine. This provides additional robustness to poor starting estimates. 7) The UI is more flexible, so that the TPMs may be refined further to include additional classes. Treating brain as only GM and WM is really a bit too simplistic. To have any chance of achieving accurate segmentation of thalamus or striatum, there needs to be additional types of GM included in the brain model. An eyeball tissue class would also help a lot ( https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0908&L=SPM&P=R13646 ). That's about it. Best regards, -John On Fri, 2010-02-12 at 13:50 +0000, João Duarte wrote: > Dear SPMers, > > in SPM8, what's the difference between the "Segment" button and the > "New Segmentation"? > > Thanks > > JD -- John Ashburner <[log in to unmask]>