niftynet.evaluation.pairwise_measures module

class niftynet.evaluation.pairwise_measures.PairwiseMeasures(seg_img, ref_img, measures=None, num_neighbors=8, pixdim=[1, 1, 1], empty=False, list_labels=None)

Bases: object

accuracy()

border_distance

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

com_dist()

com_ref()

com_seg()

connected_elements()

connected_errormaps

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

detection_error()

dice_score()

false_positive_rate()

fn

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

header_str()

informedness()

intersection_over_union()

jaccard()

list_labels()

markedness()

measured_average_distance()

measured_distance()

measured_hausdorff_distance()

n_intersection

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_neg_seg

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

n_pos_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_union

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

negative_predictive_values()

outline_error()

positive_predictive_values()

sensitivity()

specificity()

tn

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

to_string(fmt='{:.4f}')

tp

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

vol_diff()

class niftynet.evaluation.pairwise_measures.PairwiseMeasuresRegression(reg_img, ref_img, measures=None)

Bases: object

header_str()

mae()

mse()

r2()

rmse()

to_string(fmt='{:.4f}')