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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.metric.binary import __surface_distances
def normalized_surface_dice(a: np.ndarray, b: np.ndarray, threshold: float, spacing: tuple = None, connectivity=1):
"""
This implementation differs from the official surface dice implementation! These two are not comparable!!!!!
The normalized surface dice is symmetric, so it should not matter whether a or b is the reference image
This implementation natively supports 2D and 3D images. Whether other dimensions are supported depends on the
__surface_distances implementation in medpy
:param a: image 1, must have the same shape as b
:param b: image 2, must have the same shape as a
:param threshold: distances below this threshold will be counted as true positives. Threshold is in mm, not voxels!
(if spacing = (1, 1(, 1)) then one voxel=1mm so the threshold is effectively in voxels)
must be a tuple of len dimension(a)
:param spacing: how many mm is one voxel in reality? Can be left at None, we then assume an isotropic spacing of 1mm
:param connectivity: see scipy.ndimage.generate_binary_structure for more information. I suggest you leave that
one alone
:return:
"""
assert all([i == j for i, j in zip(a.shape, b.shape)]), "a and b must have the same shape. a.shape= %s, " \
"b.shape= %s" % (str(a.shape), str(b.shape))
if spacing is None:
spacing = tuple([1 for _ in range(len(a.shape))])
a_to_b = __surface_distances(a, b, spacing, connectivity)
b_to_a = __surface_distances(b, a, spacing, connectivity)
numel_a = len(a_to_b)
numel_b = len(b_to_a)
tp_a = np.sum(a_to_b <= threshold) / numel_a
tp_b = np.sum(b_to_a <= threshold) / numel_b
fp = np.sum(a_to_b > threshold) / numel_a
fn = np.sum(b_to_a > threshold) / numel_b
dc = (tp_a + tp_b) / (tp_a + tp_b + fp + fn + 1e-8) # 1e-8 just so that we don't get div by 0
return dc
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