nnUNet_calvingfront_detection
/
nnunet
/experiment_planning
/alternative_experiment_planning
/experiment_planner_baseline_3DUNet_v22.py
# 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 nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \ | |
ExperimentPlanner3D_v21 | |
from nnunet.paths import * | |
class ExperimentPlanner3D_v22(ExperimentPlanner3D_v21): | |
""" | |
""" | |
def __init__(self, folder_with_cropped_data, preprocessed_output_folder): | |
super().__init__(folder_with_cropped_data, preprocessed_output_folder) | |
self.data_identifier = "nnUNetData_plans_v2.2" | |
self.plans_fname = join(self.preprocessed_output_folder, | |
"nnUNetPlansv2.2_plans_3D.pkl") | |
def get_target_spacing(self): | |
spacings = self.dataset_properties['all_spacings'] | |
sizes = self.dataset_properties['all_sizes'] | |
target = np.percentile(np.vstack(spacings), self.target_spacing_percentile, 0) | |
target_size = np.percentile(np.vstack(sizes), self.target_spacing_percentile, 0) | |
target_size_mm = np.array(target) * np.array(target_size) | |
# we need to identify datasets for which a different target spacing could be beneficial. These datasets have | |
# the following properties: | |
# - one axis which much lower resolution than the others | |
# - the lowres axis has much less voxels than the others | |
# - (the size in mm of the lowres axis is also reduced) | |
worst_spacing_axis = np.argmax(target) | |
other_axes = [i for i in range(len(target)) if i != worst_spacing_axis] | |
other_spacings = [target[i] for i in other_axes] | |
other_sizes = [target_size[i] for i in other_axes] | |
has_aniso_spacing = target[worst_spacing_axis] > (self.anisotropy_threshold * max(other_spacings)) | |
has_aniso_voxels = target_size[worst_spacing_axis] * self.anisotropy_threshold < min(other_sizes) | |
# we don't use the last one for now | |
#median_size_in_mm = target[target_size_mm] * RESAMPLING_SEPARATE_Z_ANISOTROPY_THRESHOLD < max(target_size_mm) | |
if has_aniso_spacing and has_aniso_voxels: | |
spacings_of_that_axis = np.vstack(spacings)[:, worst_spacing_axis] | |
target_spacing_of_that_axis = np.percentile(spacings_of_that_axis, 10) | |
# don't let the spacing of that axis get higher than self.anisotropy_thresholdxthe_other_axes | |
target_spacing_of_that_axis = max(max(other_spacings) * self.anisotropy_threshold, target_spacing_of_that_axis) | |
target[worst_spacing_axis] = target_spacing_of_that_axis | |
return target | |