xcp_d.utils.qcmetrics module

Quality control metrics.

xcp_d.utils.qcmetrics.compute_dvars(*, datat, remove_zerovariance=True, variance_tol=1e-07)

Compute standard DVARS.

Parameters:

datat (numpy.ndarray) – The data matrix from which to calculate DVARS. Ordered as vertices by timepoints.

Returns:

• numpy.ndarray – The calculated DVARS array. A (timepoints,) array.

• numpy.ndarray – The calculated standardized DVARS array. A (timepoints,) array.

xcp_d.utils.qcmetrics.compute_registration_qc(bold_mask_anatspace, anat_mask_anatspace, bold_mask_stdspace, template_mask)

Compute quality of registration metrics.

This function will calculate a series of metrics, including:

• Dice’s similarity index,

• Pearson correlation coefficient, and

• Coverage

between the BOLD-to-T1w brain mask and the T1w mask, as well as between the BOLD-to-template brain mask and the template mask.

Parameters:

• bold_mask_anatspace (str) – Path to the BOLD brain mask in anatomical (T1w or T2w) space.

• anat_mask_anatspace (str) – Path to the anatomically-derived brain mask in anatomical space.

• bold_mask_stdspace (str) – Path to the BOLD brain mask in template space.

• template_mask (str) – Path to the template’s official brain mask.

Returns:

• reg_qc (dict) – Quality control measures between different inputs.

• qc_metadata (dict) – Metadata describing the QC measures.

xcp_d.utils.qcmetrics.dice(input1, input2)

Calculate Dice coefficient between two arrays.

Computes the Dice coefficient (also known as Sorensen index) between two binary images.

The metric is defined as

\[DC=\frac{2|A\cap B|}{|A|+|B|}\]

, where \(A\) is the first and \(B\) the second set of samples (here: binary objects). This method was first proposed in Dice[1] and Sorensen[2].

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – The Dice coefficient between input1 and input2. It ranges from 0 (no overlap) to 1 (perfect overlap).

Return type:

float

References

[1]

Lee R Dice. Measures of the amount of ecologic association between species. Ecology, 26(3):297–302, 1945. URL: https://doi.org/10.2307/1932409, doi:10.2307/1932409.

[2]

Th A Sorensen. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Biol. Skar., 5:1–34, 1948.

xcp_d.utils.qcmetrics.overlap(input1, input2)

Calculate overlap coefficient between two images.

The metric is defined as

\[DC=\frac{|A \cap B||}{min(|A|,|B|)}\]

, where \(A\) is the first and \(B\) the second set of samples (here: binary objects).

The overlap coefficient is also known as the Szymkiewicz-Simpson coefficient [3].

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – Coverage between two images.

Return type:

float

References

[3]

MK Vijaymeena and K Kavitha. A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(2):19–28, 2016. URL: https://doi.org/10.5121/mlaij.2016.3103, doi:10.5121/mlaij.2016.3103.

xcp_d.utils.qcmetrics.pearson(input1, input2)

Calculate Pearson product moment correlation between two images.

Parameters:

input1/input2 (numpy.ndarray) – Numpy arrays to compare. Can be any type but will be converted into binary: False where 0, True everywhere else.

Returns:

coef – Correlation between the two images.

Return type:

float