# 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