niftynet.evaluation.pairwise_measures module

class PairwiseMeasures(seg_img, ref_img, measures=None, num_neighbors=8, pixdim=(1, 1, 1), empty=False, list_labels=None)[source]

Bases: object

check_binary()[source]

Checks whether self.seg and self.ref are binary. This is to enable measurements such as ‘false positives’, which only have meaning in the binary case (what is positive/negative for multiple class?)

n_pos_ref

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

n_neg_ref

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

n_pos_seg

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

n_neg_seg

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

fp

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

fn

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

tp

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

tn

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

n_intersection

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

n_union

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

sensitivity()[source]
specificity()[source]
accuracy()[source]
false_positive_rate()[source]
positive_predictive_values()[source]
negative_predictive_values()[source]

This function calculates the negative predictive value ratio between the number of true negatives and the total number of negative elements

Returns:
dice_score()[source]

This function returns the dice score coefficient between a reference and segmentation images

Returns:dice score
intersection_over_union()[source]

This function the intersection over union ratio - Definition of jaccard coefficient

Returns:
jaccard()[source]

This function returns the jaccard coefficient (defined as intersection over union)

Returns:jaccard coefficient
informedness()[source]

This function calculates the informedness between the segmentation and the reference

Returns:informedness
markedness()[source]

This functions calculates the markedness :return:

com_dist()[source]

This function calculates the euclidean distance between the centres of mass of the reference and segmentation.

Returns:
com_ref()[source]

This function calculates the centre of mass of the reference segmentation

Returns:
com_seg()[source]

This functions provides the centre of mass of the segmented element

Returns:
list_labels()[source]
vol_diff()[source]

This function calculates the ratio of difference in volume between the reference and segmentation images.

Returns:vol_diff
border_distance

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

measured_distance()[source]

This functions calculates the average symmetric distance and the hausdorff distance between a segmentation and a reference image

Returns:hausdorff distance and average symmetric distance
measured_average_distance()[source]

This function returns only the average distance when calculating the distances between segmentation and reference

Returns:
measured_hausdorff_distance()[source]

This function returns only the hausdorff distance when calculated the distances between segmentation and reference

Returns:
connected_elements()[source]

This function returns the number of FP FN and TP in terms of connected components.

Returns:Number of true positive connected components, Number of false positives connected components, Number of false negatives connected components
connected_errormaps

this provides a decorator to cache function outputs to avoid repeating some heavy function computations

outline_error()[source]

This function calculates the outline error as defined in Wack et al.

Returns:OER: Outline error ratio, OEFP: number of false positive outlier error voxels, OEFN: number of false negative outline error elements
detection_error()[source]

This function calculates the volume of detection error as defined in Wack et al.

Returns:DE: Total volume of detection error, DEFP: Detection error false positives, DEFN: Detection error false negatives
header_str()[source]
to_string(fmt='{:.4f}')[source]
class PairwiseMeasuresRegression(reg_img, ref_img, measures=None)[source]

Bases: object

mse()[source]
rmse()[source]
mae()[source]
r2()[source]
header_str()[source]
to_string(fmt='{:.4f}')[source]