AFQ.utils.volume#

Attributes#

logger

Functions#

`transform_roi`(roi, mapping, is_boolean)	After being non-linearly transformed, ROIs tend to have holes in them.
`density_map`(tractogram[, n_sls, normalize])	Create a streamline density map.
`dice_coeff`(arr1, arr2[, weighted])	Compute Dice's coefficient between two images.

Module Contents#

AFQ.utils.volume.logger[source]#

AFQ.utils.volume.transform_roi(roi, mapping, is_boolean)[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.
is_booleanbool: Whether the ROI is boolean or not.

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

[Cousineau2017]

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