import os import json import SimpleITK as sitk import glob try: import pandas as pd except ImportError: pd = None def load_dicom_images(folder_path): reader = sitk.ImageSeriesReader() dicom_names = reader.GetGDCMSeriesFileNames(folder_path) reader.SetFileNames(dicom_names) image = reader.Execute() return image def convert_windows_to_linux_path(windows_path): # Replace backslashes with forward slashes and remove the drive letter # Some meta files have windows paths, but the data is stored on a linux server linux_path = windows_path.replace('\\', '/') if ':' in linux_path: linux_path = linux_path.split(':', 1)[1] return linux_path # ============================================================================= # ========================developed with TotalSegmentor======================== # ============================================================================= def read_table(file_path, split_str=';'): try: df = pd.read_excel(file_path, engine='openpyxl') except: df = pd.read_csv(file_path, sep=split_str) return df def load_nifti(image_path): return sitk.ReadImage(image_path) def save_nifti(image, output_path, folder_path): output_dirpath = os.path.dirname(output_path) if not os.path.exists(output_dirpath): print(f"Creating directory {output_dirpath}") os.makedirs(output_dirpath) # Set metadata in the NIfTI file's header image.SetMetaData("FolderPath", folder_path) sitk.WriteImage(image, output_path) def find_metadata_files(path, file_name='*meta*'): # for TotalSegmentor dataset search_pattern = os.path.join(path, '**', file_name) return glob.glob(search_pattern, recursive=True) def get_img_path_from_folder(folder_path, img_type='.nii.gz', include_str=None, exclude_str='segmentation', is_sorted=True): img_path = [] for root, dirs, files in os.walk(folder_path): for file in files: if file.endswith(img_type) and (include_str is None or include_str in file) and (exclude_str is None or exclude_str not in file): img_path.append(os.path.join(root, file)) if is_sorted: img_path.sort() return img_path def get_unisize_resampler(ref_img, interpolator='linear', spacing=None, size=None): ''' Resample the image to have isotropic spacing, following the steps: 1. Find the minimum spacing 2. Resample the image to have the minimum spacing 3. Set the interpolator (linear for images, nearest for segmentation masks) 4. Set the output spacing 5. Return the resampler for resampling For example, if the input image has spacing [0.1, 0.1, 0.3], the output image will have spacing [0.1, 0.1, 0.1] ''' # 讨论为什么重新写这个函数！！！ if size is None: size = ref_img.GetSize() if spacing is None: spacing = ref_img.GetSpacing() min_spacing = min(spacing) if all([spc == min_spacing for spc in spacing]): return None else: # if 1: if interpolator == 'nearest': interpolator = sitk.sitkNearestNeighbor elif interpolator == 'linear': interpolator = sitk.sitkLinear resampler = sitk.ResampleImageFilter() # new_spacing = [max_spacing] * len(spacing) # print(size) new_size = [int(round(old_sz * old_spc / min_spacing)) for old_sz, old_spc in zip(size, spacing)] new_size_xy=[new_size[0],new_size[1],new_size[2]] # 讨论为什么重新写这个函数！！！ --- YHM Jachin new_size_spacing=[min_spacing,min_spacing,min_spacing] # 讨论为什么重新写这个函数！！！ --- YHM Jachin # resampler.SetSize(new_size) # resampler.SetOutputSpacing([min_spacing] * len(spacing)) resampler.SetSize(new_size_xy) resampler.SetOutputSpacing(new_size_spacing) # print(new_size,new_size_xy) resampler.SetOutputOrigin(ref_img.GetOrigin()) resampler.SetOutputDirection(ref_img.GetDirection()) resampler.SetInterpolator(interpolator) resampler.SetDefaultPixelValue(ref_img.GetPixelIDValue()) resampler.SetOutputPixelType(ref_img.GetPixelID()) return resampler def clamp_image(in_img,clamp_range): ''' Clamp the image to the specified range ''' clamp_filter = sitk.ClampImageFilter() clamp_filter.SetLowerBound(clamp_range[0]) clamp_filter.SetUpperBound(clamp_range[1]) return clamp_filter.Execute(in_img) def get_synonyms_dict(dict_type='ROI'): ''' Get the dictionary of synonyms for the specified dictionary type ''' if dict_type == 'ROI': dict_synonyms = { 'whole-body': ['whole-body', 'whole body', 'wholebody', 'whole body', 'whole-body', 'whole body', 'wholebody','polytrauma','head-neck-thorax-abdomen-pelvis-leg','head-neck-thorax-abdomen-pelvis'], 'neck-thorax-abdomen-pelvis-leg': ['neck-thorax-abdomen-pelvis-leg','neck-thx-abd-pelvis-leg', 'angiography neck-thx-abd-pelvis-leg', 'neck thorax abdomen pelvis leg', 'neck and thorax and abdomen and pelvis and leg', 'neck, thorax, abdomen, pelvis & leg', 'neck/thorax/abdomen/pelvis/leg', 'neck, thorax, abdomen, pelvis and leg', 'neck thorax abdomen pelvis leg'], 'neck-thorax-abdomen-pelvis': ['neck-thorax-abdomen-pelvis', 'neck-thx-abd-pelvis', 'neck thorax abdomen pelvis', 'neck and thorax and abdomen and pelvis', 'neck, thorax, abdomen & pelvis', 'neck/thorax/abdomen/pelvis', 'neck, thorax, abdomen and pelvis', 'neck thorax abdomen & pelvis'], 'thorax-abdomen-pelvis-leg': ['thorax-abdomen-pelvis-leg','thx-abd-pelvis-leg', 'angiography thx-abd-pelvis-leg', 'thorax abdomen pelvis leg', 'thorax and abdomen and pelvis and leg', 'thorax, abdomen, pelvis & leg', 'thorax/abdomen/pelvis/leg', 'thorax, abdomen, pelvis and leg', 'thorax abdomen pelvis leg'], 'neck-thorax-abdomen': ['neck-thorax-abdomen', 'neck-thorax-abdomen', 'neck thorax abdomen', 'neck and thorax and abdomen', 'neck, thorax, abdomen', 'neck/thorax/abdomen', 'neck, thorax, abdomen', 'neck thorax abdomen'], 'head-neck-thorax-abdomen': ['head-neck-thorax-abdomen', 'head-neck-thorax-abdomen', 'head neck thorax abdomen', 'head and neck and thorax and abdomen', 'head, neck, thorax, abdomen', 'head/thorax/abdomen', 'head, thorax, abdomen', 'head thorax abdomen'], 'head-neck-thorax': ['head-neck-thorax', 'head neck thorax', 'head and neck and thorax', 'head, neck, thorax', 'head/thorax', 'head, thorax', 'head thorax'], 'thorax-abdomen-pelvis': ['thorax-abdomen-pelvis', 'thx-abd-pelvis', 'polytrauma', 'thorax abdomen pelvis', 'thorax and abdomen and pelvis', 'thorax, abdomen & pelvis', 'thorax/abdomen/pelvis', 'thorax, abdomen and pelvis', 'thorax abdomen & pelvis'], 'abdomen-pelvis-leg': ['abdomen-pelvis-leg', 'angiography abdomen-pelvis-leg', 'abd-pelvis-leg', 'abdomen pelvis leg', 'abdomen and pelvis and leg', 'abdomen, pelvis & leg', 'abdomen/pelvis/leg', 'abdomen, pelvis, leg', 'abdomen pelvis leg'], 'neck-thorax': ['neck-thorax', 'neck thorax', 'neck and thorax', 'neck, thorax', 'thorax-neck', 'thorax neck', 'thorax and neck', 'thorax, neck','thorax/neck'], 'thorax-abdomen': ['thorax-abdomen', 'thorax abdomen', 'thorax and abdomen', 'thorax, abdomen'], 'abdomen-pelvis': ['abdomen-pelvis', 'abdomen pelvis', 'abdomen and pelvis', 'abdomen & pelvis', 'abdomen/pelvis', 'abdomen-pelvis', 'abdomen pelvis', 'abdomen and pelvis', 'abdomen & pelvis', 'abdomen/pelvis'], 'pelvis-leg': ['pelvis-leg', 'pelvis leg', 'pelvis and leg', 'pelvis, leg', 'pelvis/leg', 'pelvis-leg', 'pelvis leg', 'pelvis and leg', 'pelvis, leg', 'pelvis/leg'], 'head-neck': ['head-neck', 'head neck', 'head and neck', 'head, neck', 'head/neck', 'head-neck', 'head neck', 'head and neck', 'head, neck', 'head/neck'], 'abdomen': ['abdomen', 'abdominal', 'belly', 'stomach', 'tummy', 'gut', 'guts', 'viscera', 'bowels', 'intestines', 'gastrointestinal', 'digestive', 'peritoneum','gastric', 'liver', 'spleen', 'pancreas','kidney','lumbar','renal','hepatic','splenic','pancreatic','intervention'], 'thorax': ['chest', 'thorax', 'breast', 'lung', 'heart','heart-thorakale aorta', 'heart-thorakale', 'mediastinum', 'pleura', 'bronchus', 'bronchi', 'trachea', 'esophagus', 'diaphragm', 'rib', 'sternum', 'clavicle', 'scapula', 'axilla', 'armpit','breast biopsy','thoracic','mammary','caeiothoracic','mediastinal','pleural','bronchial','bronchial tree','tracheal','esophageal','diaphragmatic','costal','sternal','clavicular','scapular','axillary','axillar','cardiac','pericardial','pericardiac','pericardium'], 'head': ['head', 'headbasis', 'brain', 'skull', 'face','nose','ear','eye','mouth','jaw','cheek','chin','forehead','temporal','parietal','occipital','frontal','mandible','maxilla','mandibular','maxillary','nasal','orbital','orbita','ocular','auricular','otic','oral','buccal','labial','lingual','palatal'], 'neck': ['neck', 'throat', 'cervical', 'thyroid', 'trachea', 'larynx', 'pharynx', 'esophagus','pharyngeal','laryngeal','cervical','thyroid','trachea','esophagus','carotid','jugular'], 'hand': ['hand', 'finger', 'thumb', 'palm', 'wrist', 'knuckle', 'fingernail', 'phalanx', 'metacarpal', 'carpal', 'radius'], 'arm': ['arm', 'forearm', 'upper arm', 'bicep', 'tricep', 'brachium', 'brachial', 'humerus', 'radius', 'ulna', 'elbow', 'shoulder', 'armpit''clavicle', 'scapula', 'acromion', 'acromioclavicular'], 'leg': ['leg', 'felsenleg','thigh', 'calf', 'shin', 'knee', 'foot', 'ankle', 'toe', 'heel', 'sole', 'arch', 'instep', 'metatarsal', 'phalanx', 'tibia', 'fibula', 'femur', 'patella', 'kneecap','achilles tendon','achilles'], 'pelvis': ['pelvis', 'hip', 'groin', 'buttock', 'gluteus', 'gluteal', 'ischium', 'pubis', 'sacrum', 'coccyx', 'acetabulum', 'iliac', 'iliac crest', 'iliac spine', 'iliac wing', 'sacroiliac', 'sacroiliac joint', 'sacroiliac ligament', 'sacroiliac spine', 'ureter', 'bladder', 'urethra', 'prostate', 'testicle', 'ovary', 'uterus',], 'skeleton': ['skeleton','bone','spine', 'back', 'vertebra', 'sacrum', 'coccyx'], } elif dict_type == 'Label_tissue': dict_synonyms = { 'liver': ['liver','hepatic'], 'spleen': ['spleen','splenic'], 'kidney': ['kidney','renal'], 'pancreas': ['pancreas','pancreatic'], 'stomach': ['stomach','gastric'], 'intestine': ['large intestine', 'small intestine','large bowel','small bowel'], 'gallbladder': ['gallbladder'], 'adrenal_gland': ['adrenal_gland','adrenal gland'], 'bladder': ['bladder'], 'prostate': ['prostate'], 'uterus': ['uterus'], 'ovary': ['ovary'], 'testicle': ['testicle'], 'lymph_node': ['lymph_node','lymph node'], 'bone': ['bone'], 'lung': ['lung'], 'heart': ['heart'], 'esophagus': ['esophagus'], 'muscle': ['muscle'], 'fat': ['fat'], 'skin': ['skin'], 'vessel': ['vessel'], 'tumor': ['tumor'], 'other': ['other'] } elif dict_type == 'Task': dict_synonyms = { 'segmentation': ['segmentation', 'seg', 'mask'], 'classification': ['classification', 'class', 'diagnosis','identify','identification'], 'localization': ['localization', 'locate', 'location', 'position'], 'registration': ['registration', 'register', 'align', 'alignment'], 'detection': ['detection', 'detect', 'find', 'locate'], 'quantification': ['quantification', 'quantify', 'measure', 'measurement'], } elif dict_type == 'Modality': dict_synonyms = { 'CT': ['CT', 'computed tomography'], 'MRI': ['MRI', 'MR', 'magnetic resonance imaging'], 'PET': ['PET', 'positron emission tomography'], 'US': ['US', 'ultrasound'], 'X-ray': ['X-ray', 'radiography'], 'SPECT': ['SPECT', 'single-photon emission computed tomlogy'], } else: raise ValueError(f"dict_type {dict_type} is not valid") return dict_synonyms def replace_synonyms(text, dict_synonyms): ''' Replace the synonyms in the text with the standard term ''' if isinstance(text,str): for key, value in dict_synonyms.items(): for v in value: if v.lower() in text.lower(): return key Warning(f"Value {text} is not in the correct format") elif isinstance(text,list): text = [replace_synonyms(t, dict_synonyms) for t in text] elif isinstance(text,dict): for key in text.keys(): # replace values in dict text[key] = replace_synonyms(text[key], dict_synonyms) # replace keys in dict for k in dict_synonyms.keys(): text[dict_synonyms[k]] = text.pop(key) return text # ============================================================================= class meta_data(object): ''' This class is used to store the metadata of the dataset ''' def __init__(self): self.config_format_path = os.path.join(os.path.dirname(__file__),'config_format.json') with open(self.config_format_path, 'r') as file: self.config_format = json.load(file) self.config = {} for key in self.config_format.keys(): if self.config_format[key]['required'] == True: self.config[key] = {} self.keytypes = self.find_all_keys_with_type() self.keytypes_flatten = self.flatten_json() self.ambiguity_keys = ['ROI', 'Label_tissue', 'Task', 'Modality'] for key in self.ambiguity_keys: ambiguity_dict = get_synonyms_dict(key) self.config_format[key]['options'] = list(ambiguity_dict.keys()) def get_ketytypes(self): return self.keytypes def get_keytypes_flatten(self): return self.keytypes_flatten def find_all_keys_with_type(self, data=None, parent_key=''): if data is None: data = self.config_format keys_with_type = {} if isinstance(data, dict): for key, value in data.items(): full_key = f"{parent_key}.{key}" if parent_key else key if isinstance(value, dict) and 'type' in value: keys_with_type[full_key] = value['type'] keys_with_type.update(self.find_all_keys_with_type(value, full_key)) elif isinstance(data, list): for index, item in enumerate(data): full_key = f"{parent_key}[{index}]" keys_with_type.update(self.find_all_keys_with_type(item, full_key)) return keys_with_type def flatten_json(self, data=None, parent_key='', sep='.'): if data is None: data = self.config_format items = {} if isinstance(data, dict): for key, value in data.items(): new_key = f"{parent_key}{sep}{key}" if parent_key else key if isinstance(value, dict): items.update(self.flatten_json(value, new_key, sep=sep)) elif isinstance(value, list): for i, item in enumerate(value): items.update(self.flatten_json(item, f"{new_key}[{i}]", sep=sep)) else: items[new_key] = value elif isinstance(data, list): for i, item in enumerate(data): items.update(self.flatten_json(item, f"{parent_key}[{i}]", sep=sep)) return items def req_check(self): self.unfilled_keys = [] for key in self.config.keys(): if self.config[key] == {}: self.unfilled_keys.append(key) if len(self.unfilled_keys) == 0: return True else: return False def type_check(self, key, value): if key not in self.config_format.keys(): print(key, "is not a valid key") return False if key == 'Modality': if value not in self.config_format[key]['options']: return False else: return True elif key == 'OriImg_path': if isinstance(value, str): return True else: return False elif key == 'Label_path' and isinstance(value, dict): for skey in value.keys(): if skey in self.config_format[key]['keys']: for kk in value[skey]: if isinstance(value[skey][kk],str): pass # if kk in self.config_format[key]['value']['keys']: # if isinstance(value[skey][kk],str): # pass # else: # return False else: return False return True elif key == 'ROI': if value not in self.config_format[key]['options']: return False else: return True elif key == 'Label_tissue' and isinstance(value, list): for i in value: if i not in self.config_format[key]['items']['options']: return False return True elif key =='Task' and isinstance(value, list): for i in value: if i not in self.config_format[key]['items']['options']: return False return True elif key == 'Spacing_mm': if isinstance(value, float): return True else: False # elif key == 'Size' and isinstance(value, list) and len(value) == 3 : elif key == 'Size' and isinstance(value, list) and len(value) >= 3 : return all(isinstance(item, int) for item in value) elif key == 'Dataset_name': if isinstance(value, str): return True else: return False ##added by yanguoiqng on 2025-08-08 elif key == 'Sub_modality': if isinstance(value, dict): return True else: return False elif key == 'Label_Dict': if isinstance(value, dict): return True else: return False def add_extra_keyvalue(self, key, value): self.config[key] = value return True def add_keyvalue(self, key, value): if key in self.ambiguity_keys: value = replace_synonyms(value, get_synonyms_dict(key)) # print(key, value) if self.type_check(key, value): self.config[key] = value return True else: Warning(f"Value {value} is not in the correct format for key {key}") pass # print(f"Value {value} is not in the correct format for key {key}") def get_meta_data(self): if self.req_check(): return self.config else: print("Not all required keys are filled", self.unfilled_keys) return False if __name__ == '__main__': meta = meta_data() print(meta.get_keytypes_flatten()) print(meta.get_ketytypes()) meta.add_keyvalue('Modality', 'CT') meta.add_keyvalue('OriImg_path', 'C:/Users/jzheng/Desktop/CT') meta.add_keyvalue('Label_path', {'ROI': {'1': 'C:/Users/jzheng/Desktop/CT/1'}, 'Tissue': {'1': 'C:/Users/jzheng/Desktop/CT/1'}}) meta.add_keyvalue('Spacing_mm', 1.5) meta.add_keyvalue('Size', [512, 512, 100]) meta.add_keyvalue('Dataset_name', 'CT') meta.add_keyvalue('Label_tissue', ['1', '2', '3']) meta.add_keyvalue('Task', ['1', '2', '3']) print(meta.get_meta_data()) meta.add_extra_key('extra', 'extra') print(meta.get_meta_data()) print(meta.get_ketytypes()) print(meta.get_keytypes_flatten) org_data_foler_path = '/home/jachin/data/Github/data/data_gen_def/DATASETS/TotalSegmentorCT_MRI/TS_CT' img_paths = get_img_path_from_folder(org_data_foler_path, img_type='.nii.gz', include_str='ct', exclude_str='segmentation') print(img_paths)