msynth.py

# 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("string")
                }
            )
            
        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 sort_file_paths(self, file_paths):
        digit_numbers = []
        for file_path in file_paths:
            for word in _DENSITY:
                if word in file_path:
                    if self.config.name == "device_data":
                        pattern = rf"{word}.(\d+\.)(\d+)"
                    elif self.config.name == "segmentation_mask":
                        pattern = rf"{word}.(\d+)"
                    match = re.search(pattern, file_path)
                    if self.config.name == "device_data":
                        digit_numbers.append(int(match.group(2)))
                    elif self.config.name == "segmentation_mask":
                        digit_numbers.append(int(match.group(1)))
                break
                    
        
        # Sort the list of numbers while keeping track of the original indices
        sorted_numbers_with_indices = sorted(enumerate(digit_numbers), key=lambda x: x[1])
    
        # Extract the sorted numbers and their original indices
        sorted_indices, sorted_numbers = zip(*sorted_numbers_with_indices)
       
        # Sort the file paths
        sorted_file_paths = [file_paths[i] for i in sorted_indices]
        
        return sorted_file_paths

    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 = []
            examples = []
            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

            data_dir = self.sort_file_paths(data_dir)

            new_data_dir = [];
            count = 1;
            loc = 0;
            while loc < len(data_dir):
                if count % 2 == 1:
                    new_data_dir.append(data_dir[loc])
                    loc += 1 
                else:
                    new_data_dir.append('None')
                count += 1

            for path in new_data_dir:
                res_dic = {}
                if path == "None":
                    res_dic["Raw"] = "None"
                    res_dic["mhd"] = "None"
                    res_dic["loc"] = "None"
                    res_dic["density"] = "None"
                    res_dic["mass_radius"] = "None"
                    
                else:
                    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

                examples.append(res_dic)

            for example in examples:
                yield key, {**example}
                key += 1
            examples = []
            
        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