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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.
from batchgenerators.utilities.file_and_folder_operations import *
from multiprocessing import Pool
from nnunet.configuration import default_num_threads
from nnunet.paths import nnUNet_raw_data, nnUNet_cropped_data
import numpy as np
import pickle
from nnunet.preprocessing.cropping import get_patient_identifiers_from_cropped_files
from skimage.morphology import label
from collections import OrderedDict
class DatasetAnalyzer(object):
def __init__(self, folder_with_cropped_data, overwrite=True, num_processes=default_num_threads):
"""
:param folder_with_cropped_data:
:param overwrite: If True then precomputed values will not be used and instead recomputed from the data.
False will allow loading of precomputed values. This may be dangerous though if some of the code of this class
was changed, therefore the default is True.
"""
self.num_processes = num_processes
self.overwrite = overwrite
self.folder_with_cropped_data = folder_with_cropped_data
self.sizes = self.spacings = None
self.patient_identifiers = get_patient_identifiers_from_cropped_files(self.folder_with_cropped_data)
assert isfile(join(self.folder_with_cropped_data, "dataset.json")), \
"dataset.json needs to be in folder_with_cropped_data"
self.props_per_case_file = join(self.folder_with_cropped_data, "props_per_case.pkl")
self.intensityproperties_file = join(self.folder_with_cropped_data, "intensityproperties.pkl")
def load_properties_of_cropped(self, case_identifier):
with open(join(self.folder_with_cropped_data, "%s.pkl" % case_identifier), 'rb') as f:
properties = pickle.load(f)
return properties
@staticmethod
def _check_if_all_in_one_region(seg, regions):
res = OrderedDict()
for r in regions:
new_seg = np.zeros(seg.shape)
for c in r:
new_seg[seg == c] = 1
labelmap, numlabels = label(new_seg, return_num=True)
if numlabels != 1:
res[tuple(r)] = False
else:
res[tuple(r)] = True
return res
@staticmethod
def _collect_class_and_region_sizes(seg, all_classes, vol_per_voxel):
volume_per_class = OrderedDict()
region_volume_per_class = OrderedDict()
for c in all_classes:
region_volume_per_class[c] = []
volume_per_class[c] = np.sum(seg == c) * vol_per_voxel
labelmap, numregions = label(seg == c, return_num=True)
for l in range(1, numregions + 1):
region_volume_per_class[c].append(np.sum(labelmap == l) * vol_per_voxel)
return volume_per_class, region_volume_per_class
def _get_unique_labels(self, patient_identifier):
seg = np.load(join(self.folder_with_cropped_data, patient_identifier) + ".npz")['data'][-1]
unique_classes = np.unique(seg)
return unique_classes
def _load_seg_analyze_classes(self, patient_identifier, all_classes):
"""
1) what class is in this training case?
2) what is the size distribution for each class?
3) what is the region size of each class?
4) check if all in one region
:return:
"""
seg = np.load(join(self.folder_with_cropped_data, patient_identifier) + ".npz")['data'][-1]
pkl = load_pickle(join(self.folder_with_cropped_data, patient_identifier) + ".pkl")
vol_per_voxel = np.prod(pkl['itk_spacing'])
# ad 1)
unique_classes = np.unique(seg)
# 4) check if all in one region
regions = list()
regions.append(list(all_classes))
for c in all_classes:
regions.append((c, ))
all_in_one_region = self._check_if_all_in_one_region(seg, regions)
# 2 & 3) region sizes
volume_per_class, region_sizes = self._collect_class_and_region_sizes(seg, all_classes, vol_per_voxel)
return unique_classes, all_in_one_region, volume_per_class, region_sizes
def get_classes(self):
datasetjson = load_json(join(self.folder_with_cropped_data, "dataset.json"))
return datasetjson['labels']
def analyse_segmentations(self):
class_dct = self.get_classes()
if self.overwrite or not isfile(self.props_per_case_file):
p = Pool(self.num_processes)
res = p.map(self._get_unique_labels, self.patient_identifiers)
p.close()
p.join()
props_per_patient = OrderedDict()
for p, unique_classes in \
zip(self.patient_identifiers, res):
props = dict()
props['has_classes'] = unique_classes
props_per_patient[p] = props
save_pickle(props_per_patient, self.props_per_case_file)
else:
props_per_patient = load_pickle(self.props_per_case_file)
return class_dct, props_per_patient
def get_sizes_and_spacings_after_cropping(self):
sizes = []
spacings = []
# for c in case_identifiers:
for c in self.patient_identifiers:
properties = self.load_properties_of_cropped(c)
sizes.append(properties["size_after_cropping"])
spacings.append(properties["original_spacing"])
return sizes, spacings
def get_modalities(self):
datasetjson = load_json(join(self.folder_with_cropped_data, "dataset.json"))
modalities = datasetjson["modality"]
modalities = {int(k): modalities[k] for k in modalities.keys()}
return modalities
def get_size_reduction_by_cropping(self):
size_reduction = OrderedDict()
for p in self.patient_identifiers:
props = self.load_properties_of_cropped(p)
shape_before_crop = props["original_size_of_raw_data"]
shape_after_crop = props['size_after_cropping']
size_red = np.prod(shape_after_crop) / np.prod(shape_before_crop)
size_reduction[p] = size_red
return size_reduction
def _get_voxels_in_foreground(self, patient_identifier, modality_id):
all_data = np.load(join(self.folder_with_cropped_data, patient_identifier) + ".npz")['data']
modality = all_data[modality_id]
mask = all_data[-1] > 0
voxels = list(modality[mask][::10]) # no need to take every voxel
return voxels
@staticmethod
def _compute_stats(voxels):
if len(voxels) == 0:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
median = np.median(voxels)
mean = np.mean(voxels)
sd = np.std(voxels)
mn = np.min(voxels)
mx = np.max(voxels)
percentile_99_5 = np.percentile(voxels, 99.5)
percentile_00_5 = np.percentile(voxels, 00.5)
return median, mean, sd, mn, mx, percentile_99_5, percentile_00_5
def collect_intensity_properties(self, num_modalities):
if self.overwrite or not isfile(self.intensityproperties_file):
p = Pool(self.num_processes)
results = OrderedDict()
for mod_id in range(num_modalities):
results[mod_id] = OrderedDict()
v = p.starmap(self._get_voxels_in_foreground, zip(self.patient_identifiers,
[mod_id] * len(self.patient_identifiers)))
w = []
for iv in v:
w += iv
median, mean, sd, mn, mx, percentile_99_5, percentile_00_5 = self._compute_stats(w)
local_props = p.map(self._compute_stats, v)
props_per_case = OrderedDict()
for i, pat in enumerate(self.patient_identifiers):
props_per_case[pat] = OrderedDict()
props_per_case[pat]['median'] = local_props[i][0]
props_per_case[pat]['mean'] = local_props[i][1]
props_per_case[pat]['sd'] = local_props[i][2]
props_per_case[pat]['mn'] = local_props[i][3]
props_per_case[pat]['mx'] = local_props[i][4]
props_per_case[pat]['percentile_99_5'] = local_props[i][5]
props_per_case[pat]['percentile_00_5'] = local_props[i][6]
results[mod_id]['local_props'] = props_per_case
results[mod_id]['median'] = median
results[mod_id]['mean'] = mean
results[mod_id]['sd'] = sd
results[mod_id]['mn'] = mn
results[mod_id]['mx'] = mx
results[mod_id]['percentile_99_5'] = percentile_99_5
results[mod_id]['percentile_00_5'] = percentile_00_5
p.close()
p.join()
save_pickle(results, self.intensityproperties_file)
else:
results = load_pickle(self.intensityproperties_file)
return results
def analyze_dataset(self, collect_intensityproperties=True):
# get all spacings and sizes
sizes, spacings = self.get_sizes_and_spacings_after_cropping()
# get all classes and what classes are in what patients
# class min size
# region size per class
classes = self.get_classes()
all_classes = []
for j in classes.keys():
all_classes.append([int(i) for i in classes[j].keys() if int(i) > 0])
# modalities
modalities = self.get_modalities()
# collect intensity information
if collect_intensityproperties:
intensityproperties = self.collect_intensity_properties(len(modalities))
else:
intensityproperties = None
# size reduction by cropping
size_reductions = self.get_size_reduction_by_cropping()
dataset_properties = dict()
dataset_properties['all_sizes'] = sizes
dataset_properties['all_spacings'] = spacings
dataset_properties['all_classes'] = all_classes
dataset_properties['modalities'] = modalities # {idx: modality name}
dataset_properties['intensityproperties'] = intensityproperties
dataset_properties['size_reductions'] = size_reductions # {patient_id: size_reduction}
save_pickle(dataset_properties, join(self.folder_with_cropped_data, "dataset_properties.pkl"))
return dataset_properties
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