[Neuroimaging] Analyzing the topology of ROIs and flood-filling in python (skimage?)

[Ariel Rokem]

Hi everyone,

Jason and I are working on a port of his AFQ system (
https://github.com/jyeatman/afq) into dipy. We've started sketching out
some notebooks on how that might work here:

https://github.com/arokem/AFQ-notebooks

The main thrust of this is in this one:

https://github.com/arokem/AFQ-notebooks/blob/master/AFQ-registration-callosum.ipynb

The first step in this process is to take a standard ROI of some part of
the brain (say, corpus callosum, which is where we are starting) and warp
it into the subject's individual brain through a non-linear registration
between the individual brain and the template brain on which the ROI was
defined (in this case MNI152). Registration works phenomenally (see cell
17), but because this is a non-linear registration, we find ourselves with
some holes in the ROI after the transformation (see cell 27 for a
sum-intensity projects). We are trying to use
scipy.ndimage.binary_fill_holes to, well, fill these holes, but that
doesn't seem to be working for us (cell 35 still has that hole...).

Any ideas about what might be going wrong? Are we using fill_holes
incorrectly? Any other tricks to do flood-filling in python? Should we be
using skimage?

Thanks!

Ariel
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