# Copyright 2022 for msynth dataset # # 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. ''' Custom dataset-builder for msynth dataset ''' import os import datasets import glob import re logger = datasets.logging.get_logger(__name__) _CITATION = """\ @article{sizikova2023knowledge, title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses}, author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo}, journal={Advances in Neural Information Processing Systems}, volume={}, pages={16764--16778}, year={2023} """ _DESCRIPTION = """\ M-SYNTH is a synthetic digital mammography (DM) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) toolkit. Curated by: Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma, Miguel Lago, Berkman Sahiner, Jana Gut Delfino, Aldo Badano License: Creative Commons 1.0 Universal License (CC0) """ _HOMEPAGE = "link to the dataset description page (FDA/CDRH/OSEL/DIDSR/VICTRE_project)" _REPO = "https://huggingface.co/datasets/didsr/msynth/resolve/main/data" # satting parameters for the URLS _LESIONDENSITY = ["1.0","1.06", "1.1"] _DOSE = ["20%","40%","60%","80%","100%"] _DENSITY = ["fatty", "dense", "hetero","scattered"] _SIZE = ["5.0","7.0", "9.0"] _DETECTOR = 'SIM' _DOSETABLE = { "dense": { "20%": '1.73e09', "40%": '3.47e09', "60%": '5.20e09', "80%": '6.94e09', "100%": '8.67e09' }, "hetero": { "20%": '2.04e09', "40%": '4.08e09', "60%": '6.12e09', "80%": '8.16e09', "100%": '1.02e10' }, "scattered": { "20%": '4.08e09', "40%": '8.16e09', "60%": '1.22e10', "80%": '1.63e10', "100%": '2.04e10' }, "fatty": { "20%": '4.44e09', "40%": '8.88e09', "60%": '1.33e10', "80%": '1.78e10', "100%": '2.22e10' } } # Links to download readme files _URLS = { "meta_data": f"{_REPO}/metadata/bounds.zip", "read_me": f"{_REPO}/README.md" } # Define the labels or classes in your dataset #_NAMES = ["raw", "mhd", "dicom", "loc"] DATA_DIR = {"all_data": "SIM", "seg": "SIM", "info": "bounds"} class msynthConfig(datasets.BuilderConfig): """msynth dataset""" lesion_density = _LESIONDENSITY dose = _DOSE density = _DENSITY size = _SIZE def __init__(self, name, **kwargs): super(msynthConfig, self).__init__( version=datasets.Version("1.0.0"), name=name, description="msynth", **kwargs, ) class msynth(datasets.GeneratorBasedBuilder): """msynth dataset.""" DEFAULT_WRITER_BATCH_SIZE = 256 BUILDER_CONFIGS = [ msynthConfig("device_data"), msynthConfig("segmentation_mask"), msynthConfig("metadata"), ] def _info(self): if self.config.name == "device_data": # Define dataset features and keys features = datasets.Features( { "Raw": datasets.Value("string"), "mhd": datasets.Value("string"), "loc": datasets.Value("string"), "dcm": datasets.Value("string"), "density": datasets.Value("string"), "mass_radius": datasets.Value("float32") } ) #keys = ("image", "metadata") elif self.config.name == "segmentation_mask": # Define features and keys features = datasets.Features( { "Raw": datasets.Value("string"), "mhd": datasets.Value("string"), "loc": datasets.Value("string"), "density": datasets.Value("string"), "mass_radius": datasets.Value("float32") } ) elif self.config.name == "metadata": # Define features and keys features = datasets.Features( { "fatty": datasets.Value("string"), "dense": datasets.Value("string"), "hetero": datasets.Value("string"), "scattered": datasets.Value("string") } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators( self, dl_manager: datasets.utils.download_manager.DownloadManager): # Setting up the **config_kwargs parameters if self.config.lesion_density == "all": self.config.lesion_density = _LESIONDENSITY if self.config.dose == "all": self.config.dose = _DOSE if self.config.density == "all": self.config.density = _DENSITY if self.config.size == "all": self.config.size = _SIZE if self.config.name == "device_data": file_name = [] for ld in self.config.lesion_density: for ds in self.config.dose: for den in self.config.density: value = _DOSETABLE[den][ds] for sz in self.config.size: temp_name = [] temp_name = ( "device_data_VICTREPhantoms_spic_" + ld + "/" + value + "/" + den + "/2/" + sz + "/" + _DETECTOR + ".zip" ) file_name.append(_REPO +"/"+ temp_name) # Downloading the data files # data_dir = dl_manager.download_and_extract(file_name) data_dir = [] for url in file_name: try: temp_down_file = [] # Attempt to download the file temp_down_file = dl_manager.download_and_extract(url) data_dir.append(temp_down_file) except Exception as e: # If an exception occurs (e.g., file not found), log the error and add the URL to the failed_urls list logger.error(f"Failed to download {url}: {e}") return [ datasets.SplitGenerator( name="device_data", gen_kwargs={ "files": [data_dir_t for data_dir_t in data_dir], "name": "all_data", }, ), ] elif self.config.name == "segmentation_mask": seg_file_name = [] for den in self.config.density: for sz in self.config.size: temp_name = [] temp_name = ( "segmentation_masks" + "/" + den + "/2/" + sz + "/" + _DETECTOR + ".zip" ) seg_file_name.append(_REPO+ "/" + temp_name) # Downloading the files seg_dir = [] #seg_dir = dl_manager.download_and_extract(seg_file_name) for url in seg_file_name: try: # Attempt to download the file temp_down_file = [] temp_down_file = dl_manager.download_and_extract(url) seg_dir.append(temp_down_file) except Exception as e: # If an exception occurs (e.g., file not found), log the error and add the URL to the failed_urls list logger.error(f"Failed to download {url}: {e}") return [ datasets.SplitGenerator( name="segmentation_mask", gen_kwargs={ "files": [data_dir_t for data_dir_t in seg_dir], "name": "seg", }, ), ] elif self.config.name == "metadata": meta_dir = dl_manager.download_and_extract(_URLS['meta_data']) return [ datasets.SplitGenerator( name="metadata", gen_kwargs={ "files": meta_dir, "name": "info", }, ), ] def get_all_file_paths(self, root_directory): file_paths = [] # List to store file paths # Walk through the directory and its subdirectories using os.walk for folder, _, files in os.walk(root_directory): for file in files: if file.endswith('.raw'): # Get the full path of the file file_path = os.path.join(folder, file) file_paths.append(file_path) return file_paths def get_support_file_path(self, raw_file_path, ext): folder_path = os.path.dirname(raw_file_path) # Use os.path.basename() to extract the filename raw_file_name = os.path.basename(raw_file_path) # Use os.path.splitext() to split the filename into root and extension root, extension = os.path.splitext(raw_file_name) if ext == "dcm": supp_file_name = f"000.{ext}" file_path = os.path.join(folder_path,"DICOM_dm",supp_file_name) else: supp_file_name = f"{root}.{ext}" file_path = os.path.join(folder_path, supp_file_name) if os.path.isfile(file_path): return file_path else: return "Not available for this raw file" def _generate_examples(self, files, name): if self.config.name == "device_data": key = 0 data_dir = [] for folder in files: tmp_dir = [] tmp_dir = self.get_all_file_paths(os.path.join(folder, DATA_DIR[name])) data_dir = data_dir + tmp_dir for path in data_dir: res_dic = {} for word in _DENSITY: if word in path: breast_density = word pattern = rf"(\d+\.\d+)_{word}" match = re.search(pattern, path) matched_text = match.group(1) break # Get image id to filter the respective row of the csv image_id = os.path.basename(path) # Use os.path.splitext() to split the filename into root and extension root, extension = os.path.splitext(image_id) # Get the extension without the dot image_labels = extension.lstrip(".") res_dic["Raw"] = path res_dic["mhd"] = self.get_support_file_path(path, "mhd") res_dic["loc"] = self.get_support_file_path(path, "loc") res_dic["dcm"] = self.get_support_file_path(path, "dcm") res_dic["density"] = breast_density res_dic["mass_radius"] = matched_text yield key, res_dic key += 1 if self.config.name == "segmentation_mask": key = 0 data_dir = [] for folder in files: tmp_dir = [] tmp_dir = self.get_all_file_paths(os.path.join(folder, DATA_DIR[name])) data_dir = data_dir + tmp_dir for path in data_dir: res_dic = {} for word in _DENSITY: if word in path: breast_density = word pattern = rf"(\d+\.\d+)_{word}" match = re.search(pattern, path) matched_text = match.group(1) break # Get image id to filter the respective row of the csv image_id = os.path.basename(path) # Use os.path.splitext() to split the filename into root and extension root, extension = os.path.splitext(image_id) # Get the extension without the dot image_labels = extension.lstrip(".") res_dic["Raw"] = path res_dic["mhd"] = self.get_support_file_path(path, "mhd") res_dic["loc"] = self.get_support_file_path(path, "loc") res_dic["density"] = breast_density res_dic["mass_radius"] = matched_text yield key, res_dic key += 1 if self.config.name == "metadata": key = 0 examples = list() meta_dir = os.path.join(files, DATA_DIR[name]) res_dic = { "fatty": os.path.join(meta_dir,'bounds_fatty.npy'), "dense": os.path.join(meta_dir,'bounds_dense.npy'), "hetero": os.path.join(meta_dir,'bounds_hetero.npy'), "scattered": os.path.join(meta_dir,'bounds_scattered.npy') } yield key, res_dic key +=1