dicom2hdf 0.1.6

Medical data formatting and pre-processing module whose main objective is to build an HDF5 dataset containing all medical images of patients (DICOM format) and their associated segmentations. The HDF5 dataset is then easier to use to perform tasks on the medical data, such as machine learning tasks.

Medical data formatting and pre-processing module

This package is a medical data formatting and pre-processing module whose main objective is to build an HDF5 dataset containing all medical images of patients (DICOM format) and their associated segmentations. The HDF5 dataset is then easier to use to perform tasks on the medical data, such as machine learning tasks.

Anyone who is willing to contribute is welcome to do so.

Motivation

Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical images and related information. It defines the formats for medical images that can be exchanged with the data and quality necessary for clinical use. The working group DICOM WG-23 on Artificial Intelligence / Application Hosting is currently working to identify or develop the DICOM mechanisms to support AI workflows, concentrating on the clinical context. Moreover, their future roadmap and objectives includes working on the concern that current DICOM mechanisms might not be adequate to cover some use cases, particularly bulk analysis of large repository data, e.g. for training deep learning neural networks. However, no tool has been developed to achieve this goal at present.

The purpose of this module is therefore to provide the necessary tools to facilitate the use of medical images in an AI workflow. This goal is accomplished by using the HDF file format to create a dataset containing all medical images of patients and their associated segmentations.

Installation

Latest stable version :

pip install dicom2hdf

Latest (possibly unstable) version :

pip install git+https://github.com/MaxenceLarose/dicom2hdf

Brief explanation of how it works

First, it is necessary to explain briefly how the package builds a patient's dataset using the provided data.

For each of the patients contained in the patients folder, all DICOM files present in the images folder are read. If the series descriptions of a certain volume match one of the descriptions present in the given series_descriptions dictionary, this volume is automatically added to the patient dataset. Then, all DICOM-SEG files present in the segmentations folder are read. Segmentation volumes whose reference series UID matches one of the series UID read from the patient's images folder are added to the patient's dataset. The reference volume is also added to the patient dataset.

Note : If no series_descriptions dictionary is given, the step of selecting the images according to this criterion is simply ignored. In the same way, if no DICOM-SEG file is present in the segmentations folder, the segmentations and images selection step according to this criterion is simply ignored.

The above description is confusing. To fully understand it, read the Getting started section.

Getting started

Organize your data

As this module requires the use of data, it is important to properly configure the data-related elements before using it. The following sections present how to configure these elements.

File format

Images must be in standard DICOM format and segmentations must be in DICOM-SEG format.

If your segmentations are in a research file format (.nrrd, .nii, etc.) and you want to convert them into the standardized DICOM-SEG format, you can use the pydicom-seg library to create the DICOM-SEG files OR use the itkimage2dicomSEG python module, which provide a complete pipeline to perform this conversion.

Series descriptions (Optional)

This dictionary is not mandatory for the code to work and therefore its default value is None.

The series descriptions are specified as a dictionary that contains the series descriptions of the images that absolutely needs to be extracted from the patients' files. Keys are arbitrary names given to the images we want to add and values are lists of series descriptions. The images associated with these series descriptions do not need to have a corresponding segmentation. In fact, the whole point of adding a way to specify which series descriptions should be added to the dataset is to be able to add images without their segmentation. Note that it can also be specified as a path to a json file that contains the series descriptions. Both methods are presented below.

Warning! This dictionary (or json file) can be modified during the execution of the package functions. THIS IS NORMAL, we potentially want to add series descriptions if none of the descriptions match the series in the patient's files.

Using a json file

Create a json file containing only the dictionary of the names given to the images we want to add (keys) and lists of series descriptions (values). Place this file in your data folder.

Here is an example of a json file configured as expected :

{
    "TEP": [
        "TEP WB CORR (AC)",
        "TEP WB XL CORR (AC)"
    ],
    "CT": [
        "CT 2.5 WB",
        "AC CT 2.5 WB"
    ]
}

Using a Python dictionary

Create the organs dictionary in your main.py python file.

Here is an example of a python dictionary instanced as expected :

series_descriptions = {
    "TEP": [
        "TEP WB CORR (AC)",
        "TEP WB XL CORR (AC)"
    ],
    "CT": [
        "CT 2.5 WB",
        "AC CT 2.5 WB"
    ]
}

Structure your patients directory

It is important to configure the directory structure correctly to ensure that the module interacts correctly with the data files. The repository, particularly the patients folder, must be structured as follows. The names of the folders and files can differ, but they must be consistent with the names written in the settings.py file (See the File names section below).

|_📂 Project directory/
  |_📄 settings.py
  |_📄 main.py
  |_📂 data/
    |_📄 series_descriptions.json
    |_📂 patients/
      |_📂 patient1/
       	|_📂 images/
       	  |_📄 IM0.dcm
       	  |_📄 IM1.dcm
       	  |_📄 ...
       	|_📂 segmentations/
       	  |_📄 IM0.SEG.dcm
       	  |_📄 IM1.SEG.dcm
       	  |_📄 ...
      |_📂 patient2/
       	|_📂 images/
       	  |_📄 IM0.dcm
       	  |_📄 ...
       	|_📂 segmentations/
       	  |_📄 IM0.SEG.dcm
       	  |_📄 ...
      |_📂 ...

File names

It is good practice to create a class that lists the various names and important paths of the folders that contain the data. I propose here a way to organize this class.

It is first necessary to populate a file named settings.py which contains the important FileName ,FolderName and PathName classes. The settings.py file must be placed in the same folder as the data folder. It is easier to understand this step with an example so here is the expected content of settings.py . You can adapt the names of folders and files to your own personal data.

from os.path import abspath, dirname, join

ROOT = abspath(dirname(__file__))


class FileName:
    SERIES_DESCRIPTIONS_JSON: str = "series_descriptions.json"
    PATIENT_DATASET_HDF5: str = "patient_dataset.h5"


class FolderName:
    DATA_FOLDER: str = "data"
    PATIENTS_FOLDER: str = "patients"
    IMAGES_FOLDER: str = "images"
    SEGMENTATIONS_FOLDER: str = "segmentations"


class PathName:
    PATH_TO_DATA_FOLDER: str = join(ROOT, FolderName.DATA_FOLDER)

    PATH_TO_SERIES_DESCRIPTIONS_JSON: str = join(PATH_TO_DATA_FOLDER, FileName.SERIES_DESCRIPTIONS_JSON)
    PATH_TO_PATIENT_DATASET_HDF5: str = join(PATH_TO_DATA_FOLDER, FileName.PATIENT_DATASET_HDF5)

    PATH_TO_PATIENTS_FOLDER: str = join(PATH_TO_DATA_FOLDER, FolderName.PATIENTS_FOLDER)

Import the package

The easiest way to import the package is to use :

from dicom2hdf import *

This will import the useful classes PatientDataset and PatientDataGenerator and the useful function logs_file_setup. These two classes represent two different ways of using the package. The following examples will present both procedures.

Use the package

The two examples below show code to add to the main.py file.

Example using the patient dataset class

This file can then be executed to obtain an hdf5 dataset.

import logging

from dicom2hdf import *

from .settings import *

logs_file_setup(level=logging.INFO)

dataset = PatientDataset(
    path_to_dataset=PathName.PATH_TO_PATIENT_DATASET_HDF5,
)

dataset.create_hdf5_dataset(
    path_to_patients_folder=PathName.PATH_TO_PATIENTS_FOLDER,
    images_folder_name=FolderName.IMAGES_FOLDER,
    segmentations_folder_name=FolderName.SEGMENTATIONS_FOLDER,
    series_descriptions=PathName.PATH_TO_SERIES_DESCRIPTIONS_JSON,
    verbose=True,
    overwrite_dataset=True
)

The created HDF5 dataset will then look something like :

Example using the patient data generator class

This file can then be executed to perform on-the-fly tasks on images.

import logging

from dicom2hdf import *
import SimpleITK as sitk

from .settings import *

logs_file_setup(level=logging.INFO)

patient_data_generator = PatientDataGenerator(
    path_to_patients_folder=PathName.PATH_TO_PATIENTS_FOLDER,
    images_folder_name=FolderName.IMAGES_FOLDER,
    segmentations_folder_name=FolderName.SEGMENTATIONS_FOLDER,
    series_descriptions=PathName.PATH_TO_SERIES_DESCRIPTIONS_JSON,
    verbose=True
)

for patient_dataset in patient_data_generator:
    patient_name = patient_dataset.patient_name
    
    for patient_image_data in patient_dataset.data:
        dicom_header = patient_image_data.image.dicom_header
        simple_itk_image = patient_image_data.image.simple_itk_image
        numpy_array_image = sitk.GetArrayFromImage(simple_itk_image)
        
        """Perform any tasks on images on-the-fly like printing the image array shape."""
        print(numpy_array_image.shape)

License

This code is provided under the Apache License 2.0.

Citation

@article{dicom2hdf,
  title={dicom2hdf: DICOM to HDF python module},
  author={Maxence Larose},
  year={2022},
  publisher={Université Laval},
  url={https://github.com/MaxenceLarose/dicom2hdf},
}

Contact

Maxence Larose, B. Ing., [e-mail]