[Neuroimaging] Analyzing the topology of ROIs and flood-filling in python (skimage?)
jomaroceguedag at gmail.com
Thu Sep 3 04:19:16 CEST 2015
Thanks Ariel, and don't worry, this is very related to the work I'm doing
now, so this is actually very useful. I almost reproduced your experiment,
by any chance can you share: LOCC_ni, ROCC_ni and midsag_ni?
On Wed, Sep 2, 2015 at 8:29 PM, Ariel Rokem <arokem at gmail.com> wrote:
> 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,
> 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!
>> 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.
>>> 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:
>>>> The main thrust of this is in this one:
>>>> 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?
>>>> Neuroimaging mailing list
>>>> Neuroimaging at python.org
>>> Neuroimaging mailing list
>>> Neuroimaging at python.org
>> "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.
>> Neuroimaging mailing list
>> Neuroimaging at python.org
> Neuroimaging mailing list
> Neuroimaging at python.org
"Cada quien es dueño de lo que calla y esclavo de lo que dice"
"We all are owners of what we keep silent and slaves of what we say"
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Neuroimaging