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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from medpy import metric
def assert_shape(test, reference):
assert test.shape == reference.shape, "Shape mismatch: {} and {}".format(
test.shape, reference.shape)
class ConfusionMatrix:
def __init__(self, test=None, reference=None):
self.tp = None
self.fp = None
self.tn = None
self.fn = None
self.size = None
self.reference_empty = None
self.reference_full = None
self.test_empty = None
self.test_full = None
self.set_reference(reference)
self.set_test(test)
def set_test(self, test):
self.test = test
self.reset()
def set_reference(self, reference):
self.reference = reference
self.reset()
def reset(self):
self.tp = None
self.fp = None
self.tn = None
self.fn = None
self.size = None
self.test_empty = None
self.test_full = None
self.reference_empty = None
self.reference_full = None
def compute(self):
if self.test is None or self.reference is None:
raise ValueError("'test' and 'reference' must both be set to compute confusion matrix.")
assert_shape(self.test, self.reference)
self.tp = int(((self.test != 0) * (self.reference != 0)).sum())
self.fp = int(((self.test != 0) * (self.reference == 0)).sum())
self.tn = int(((self.test == 0) * (self.reference == 0)).sum())
self.fn = int(((self.test == 0) * (self.reference != 0)).sum())
self.size = int(np.prod(self.reference.shape, dtype=np.int64))
self.test_empty = not np.any(self.test)
self.test_full = np.all(self.test)
self.reference_empty = not np.any(self.reference)
self.reference_full = np.all(self.reference)
def get_matrix(self):
for entry in (self.tp, self.fp, self.tn, self.fn):
if entry is None:
self.compute()
break
return self.tp, self.fp, self.tn, self.fn
def get_size(self):
if self.size is None:
self.compute()
return self.size
def get_existence(self):
for case in (self.test_empty, self.test_full, self.reference_empty, self.reference_full):
if case is None:
self.compute()
break
return self.test_empty, self.test_full, self.reference_empty, self.reference_full
def dice(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""2TP / (2TP + FP + FN)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty and reference_empty:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(2. * tp / (2 * tp + fp + fn))
def jaccard(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TP / (TP + FP + FN)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty and reference_empty:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(tp / (tp + fp + fn))
def precision(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TP / (TP + FP)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(tp / (tp + fp))
def sensitivity(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TP / (TP + FN)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if reference_empty:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(tp / (tp + fn))
def recall(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TP / (TP + FN)"""
return sensitivity(test, reference, confusion_matrix, nan_for_nonexisting, **kwargs)
def specificity(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TN / (TN + FP)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if reference_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(tn / (tn + fp))
def accuracy(test=None, reference=None, confusion_matrix=None, **kwargs):
"""(TP + TN) / (TP + FP + FN + TN)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
return float((tp + tn) / (tp + fp + tn + fn))
def fscore(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, beta=1., **kwargs):
"""(1 + b^2) * TP / ((1 + b^2) * TP + b^2 * FN + FP)"""
precision_ = precision(test, reference, confusion_matrix, nan_for_nonexisting)
recall_ = recall(test, reference, confusion_matrix, nan_for_nonexisting)
return (1 + beta*beta) * precision_ * recall_ /\
((beta*beta * precision_) + recall_)
def false_positive_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""FP / (FP + TN)"""
return 1 - specificity(test, reference, confusion_matrix, nan_for_nonexisting)
def false_omission_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""FN / (TN + FN)"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0.
return float(fn / (fn + tn))
def false_negative_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""FN / (TP + FN)"""
return 1 - sensitivity(test, reference, confusion_matrix, nan_for_nonexisting)
def true_negative_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TN / (TN + FP)"""
return specificity(test, reference, confusion_matrix, nan_for_nonexisting)
def false_discovery_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""FP / (TP + FP)"""
return 1 - precision(test, reference, confusion_matrix, nan_for_nonexisting)
def negative_predictive_value(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):
"""TN / (TN + FN)"""
return 1 - false_omission_rate(test, reference, confusion_matrix, nan_for_nonexisting)
def total_positives_test(test=None, reference=None, confusion_matrix=None, **kwargs):
"""TP + FP"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
return tp + fp
def total_negatives_test(test=None, reference=None, confusion_matrix=None, **kwargs):
"""TN + FN"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
return tn + fn
def total_positives_reference(test=None, reference=None, confusion_matrix=None, **kwargs):
"""TP + FN"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
return tp + fn
def total_negatives_reference(test=None, reference=None, confusion_matrix=None, **kwargs):
"""TN + FP"""
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
tp, fp, tn, fn = confusion_matrix.get_matrix()
return tn + fp
def hausdorff_distance(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty or test_full or reference_empty or reference_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0
test, reference = confusion_matrix.test, confusion_matrix.reference
return metric.hd(test, reference, voxel_spacing, connectivity)
def hausdorff_distance_95(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty or test_full or reference_empty or reference_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0
test, reference = confusion_matrix.test, confusion_matrix.reference
return metric.hd95(test, reference, voxel_spacing, connectivity)
def avg_surface_distance(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty or test_full or reference_empty or reference_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0
test, reference = confusion_matrix.test, confusion_matrix.reference
return metric.asd(test, reference, voxel_spacing, connectivity)
def avg_surface_distance_symmetric(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):
if confusion_matrix is None:
confusion_matrix = ConfusionMatrix(test, reference)
test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()
if test_empty or test_full or reference_empty or reference_full:
if nan_for_nonexisting:
return float("NaN")
else:
return 0
test, reference = confusion_matrix.test, confusion_matrix.reference
return metric.assd(test, reference, voxel_spacing, connectivity)
ALL_METRICS = {
"False Positive Rate": false_positive_rate,
"Dice": dice,
"Jaccard": jaccard,
"Hausdorff Distance": hausdorff_distance,
"Hausdorff Distance 95": hausdorff_distance_95,
"Precision": precision,
"Recall": recall,
"Avg. Symmetric Surface Distance": avg_surface_distance_symmetric,
"Avg. Surface Distance": avg_surface_distance,
"Accuracy": accuracy,
"False Omission Rate": false_omission_rate,
"Negative Predictive Value": negative_predictive_value,
"False Negative Rate": false_negative_rate,
"True Negative Rate": true_negative_rate,
"False Discovery Rate": false_discovery_rate,
"Total Positives Test": total_positives_test,
"Total Negatives Test": total_negatives_test,
"Total Positives Reference": total_positives_reference,
"total Negatives Reference": total_negatives_reference
}
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