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

[Ariel Rokem]

Hi Omar,

Excellent - thanks so much for taking a look! I know that you are very busy
these days, and so your attention on this is highly appreciated! I will try
experimenting more with this, with different input parameters, as you
suggested.

If you also want to take a look, since #680 and #681 were merged into dipy,
you can now run:

    import dipy.data as dpd
    MNI_T2 = dpd.read_mni_template()

To get the template data.

Thanks again,

Ariel

On Wed, Sep 2, 2015 at 6:16 PM, Jesus-Omar Ocegueda-Gonzalez <
jomaroceguedag at gmail.com> wrote:

> Hello guys!,
> I have been working on this issue for some days now (this is very
> interesting Ariel!, thanks for sharing your findings). Satra is totally
> right that **in theory** the transformations should preserve the topology.
> Unfortunately, the transformations are only **approximately**
> diffeomorphic. I am totally sure that this issue should be there in the
> original version of ants too (dipy's implementation is the same algorithm),
> although maybe the new version (antsRegistration) may have some
> improvements that I'm not aware of.
>
> Having said that, you can make the transforms closer to diffeomorphic by
> reducing the `step_length` parameter (in millimeters) from
> `SymmetricDiffeomorphicRegistration`, which by default is 0.25 mm. You may
> try something about 0.15 mm. The objective is to avoid making very
> "aggressive" iterations, so another way to achieve this is by increasing
> the smoothing parameter from the CCMetric, the parameter is `sigma_diff`,
> which by default is 2.0, you may try something bout 3.0 (I would first try
> reducing the step size, though).
>
> I would like to try some other ideas, by any chance can you share the data
> (MNI_T2)?
> Thank you very much!
> -Omar.
>
>
>
> On Wed, Sep 2, 2015 at 7:28 PM, Satrajit Ghosh <satra at mit.edu> wrote:
>
>> hi ariel,
>>
>> can you do nearest neighbor interpolation in `mapping.inverse_transform`?
>> if your original ROI doesn't have holes and you are doing a diffeomorphic
>> mapping, your target shouldn't have holes either. for a comparison you
>> could run antsRegister and antsApplyTransforms, with nearest neighbor
>> interpolation.
>>
>> cheers,
>>
>> satra
>>
>> On Wed, Sep 2, 2015 at 8:13 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> 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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>>>
>>>
>>
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>
>
> --
> "Cada quien es dueño de lo que calla y esclavo de lo que dice"
> -Proverbio chino.
> "We all are owners of what we keep silent and slaves of what we say"
> -Chinese proverb.
>
> http://www.cimat.mx/~omar
>
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