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

Thu Sep 3 17:24:25 CEST 2015

Hi Ariel,
I checked the transformation and everything seems to be correct. The hole
is an interpolation artifact. The problem is that the ROI is too thin (only
2 voxels), if you visualize the warped voxels as points in 3D you can see
that there is no actual "hole", but it is generated by removing two voxels
from the boundary of two different warped slices (a valid result from a
diffeomorphic map). To illustrate this, I dilated the mask along the x-axis
by one voxel (now the thickness of the mask is 3 voxels) and warped the
dilated mask with the same transform, after doing this, I get this ROI:
[image: Inline image 1]
Anyway, the discussion about the transforms being only **approximately**
diffeomorphic is still valid but it is not a problem in this particular
case.

On Wed, Sep 2, 2015 at 11:50 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Hi Omar,
>
> The other ROIs are here:
> https://github.com/jyeatman/AFQ/tree/master/templates. I will think about
> the rest of your response tomorrow!
>
> Cheers,
>
> Ariel
>
> On Wed, Sep 2, 2015 at 7:39 PM, Jesus-Omar Ocegueda-Gonzalez <
> jomaroceguedag at gmail.com> wrote:
>
>> Actually, Ariel, nearest neighbor interpolation is a very unstable
>> operation. If you interpolate at x or x+epsilon you may get different
>> results for a very small epsilon, and discarding one single voxel may lead
>> to a rejection of a large number of streamlines (I'm thinking about the
>> boundary of the ROI too!, not only the "hole" ). I think it would be a more
>> precise selection if you warped the streamlines to the template and select
>> them there (now I see that we need that extension to the diffeomorphic map
>> asap!).
>>
>> On Wed, Sep 2, 2015 at 9:19 PM, Jesus-Omar Ocegueda-Gonzalez <
>> jomaroceguedag at gmail.com> wrote:
>>
>>> 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,
>>>>
>>>> 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
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Neuroimaging mailing list
>>>>>>> Neuroimaging at python.org
>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging
>>>>>>>
>>>>>>>
>>>>>>
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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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>>>>>
>>>>
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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
>>>
>>
>>
>>
>> --
>> "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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>>
>
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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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