[Neuroimaging] Analyzing the topology of ROIs and flood-filling in python (skimage?)
Bob Dougherty
bobd at stanford.edu
Thu Sep 3 23:07:22 CEST 2015
A 'hole' in 3d is a voxel fully surrounded by non-zero voxels. If the
projection to 2d shows a 'hole', then it's not a hole in the 3-d sense,
but rather a worm-hole that connects to the outside of the object (in
this case, straight through along the projection axis). So 3d
hole-filling won't fix it. You could try dilate-erode (i.e., image
closing), but that will remove some detail.
I can see how such things would be created by a non-linear deformation,
especially with a very thin ROI. E.g., imagine that the deformation
field piches together your CC ROI in that region, pushing the left and
right bounds of the ROI very close together. Then when you interpolate,
some voxels in the pinched region might disappear.
I think avoiding ROIs with very thin structures would be wise. And maybe
a little image closing as well. Also, are you using trilinear
interpolation followed by a threshold to get back to a binary image?
On 09/03/2015 01:42 PM, Ariel Rokem wrote:
> This is great - I think that ultimately the solution very well be to
> expand the ROI, as you propose.
>
> But I am not still not sure that I follow your reasoning: are you
> saying that what it only seems like a hole, but there is actually no
> hole in the resulting deformed ROI? Note that I am summing across that
> dimension in displaying the mask in cell 27 of the notebook I shared.
> As I understand it, the only way there could be a 0-valued voxel in
> the middle of the ROI is if there is a topological hole in the ROI.
>
> Which leads me back to my original question: if I have an object
> represented as a binary mask in a 3D array and I wonder whether it's
> topologically a torus or a sphere, how do I go about calculating that?
> Furthermore - why does ndimage.fill_holes not seem to fill that hole?
> (maybe there's no hole? Is that what you meant?).
>
> Thanks again!
>
> Ariel
>
>
> On Thu, Sep 3, 2015 at 8:24 AM, Jesus-Omar Ocegueda-Gonzalez
> <jomaroceguedag at gmail.com <mailto:jomaroceguedag at gmail.com>> wrote:
>
> 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:
> 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
> <mailto: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 <mailto: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
> <mailto: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 <mailto: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
> <mailto: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 <mailto: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
> <mailto: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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> "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 <http://www.cimat.mx/%7Eomar>
>
>
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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 <http://www.cimat.mx/%7Eomar>
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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 <http://www.cimat.mx/%7Eomar>
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--
Robert F. Dougherty, PhD
Research Director
Stanford Center for Cognitive and Neurobiological Imaging
70 Jordan Hall * Stanford CA 94305 * 650-725-0051
http://www.stanford.edu/~bobd
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