AFQ.utils.volume#
Module Contents#
Functions#
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After being non-linearly transformed, ROIs tend to have holes in them. |
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After being non-linearly transformed, ROIs tend to have holes in them. |
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Create a streamline density map. |
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Compute Dice's coefficient between two images. |
Attributes#
- AFQ.utils.volume.transform_inverse_roi(roi, mapping, bundle_name='ROI')[source]#
After being non-linearly transformed, ROIs tend to have holes in them. We perform a couple of computational geometry operations on the ROI to fix that up.
- Parameters
- roiNifti1Image, str, ndarray
The ROI to transform. Can be a path or image, which will be converted to an ndarray.
- mappingDiffeomorphicMap object
A mapping between DWI space and a template.
- bundle_namestr, optional
Name of bundle, which may be useful for error messages. Default: None
- Returns
- ROI after dilation and hole-filling
- AFQ.utils.volume.patch_up_roi(roi, bundle_name='ROI')[source]#
After being non-linearly transformed, ROIs tend to have holes in them. We perform a couple of computational geometry operations on the ROI to fix that up.
- Parameters
- roi3D binary array
The ROI after it has been transformed.
- bundle_namestr, optional
Name of bundle, which may be useful for error messages. Default: None
- Returns
- ROI after dilation and hole-filling
- AFQ.utils.volume.density_map(tractogram, n_sls=None, normalize=False)[source]#
Create a streamline density map. based on: https://dipy.org/documentation/1.1.1./examples_built/streamline_formats/
- Parameters
- tractogramStatefulTractogram
Stateful tractogram whose streamlines are used to make the density map.
- n_slsint or None, optional
n_sls to randomly select to make the density map. If None, all streamlines are used. Default: None
- normalizebool, optional
Whether to normalize maximum values to 1. Default: False
- Returns
- Nifti1Image containing the density map.
- AFQ.utils.volume.dice_coeff(arr1, arr2, weighted=True)[source]#
Compute Dice’s coefficient between two images.
- Parameters
- arr1Nifti1Image, str, ndarray
One ndarray to compare. Can be a path or image, which will be converted to an ndarray.
- arr2Nifti1Image, str, ndarray
The other ndarray to compare. Can be a path or image, which will be converted to an ndarray.
- weightedbool, optional
Whether or not to weight the DICE coefficient as in [Cousineau2017]. The weighted Dice coefficient is calculated by adding the sum of all values in arr1 where arr2 is nonzero to the sum of all values in arr2 where arr1 is nonzero, then dividing that by the sum of all values in arr1 and arr2. Default: True
- Returns
- The dice similarity between the images.
Notes
- 1
Cousineau M, Jodoin PM, Morency FC, et al. A test-retest study on Parkinson’s PPMI dataset yields statistically significant white matter fascicles. Neuroimage Clin. 2017;16:222-233. Published 2017 Jul 25. doi:10.1016/j.nicl.2017.07.020