Package: med-imagetools
Med-Imagetools: Transparent and Reproducible Medical Image Processing Pipelines in PythonLatest Updates (v1.2.0) - Feb 5th, 2024Documentation is now available at:https://med-imagetools.readthedocs.ioDependencies have been reduced for a lighter install.torchandtorchiodependencies have been moved to an extra pip install flag. Usepip install med-imagetools[torch]to use the Dataset feature andMed-ImageTools core featuresAutoPipeline CLInnunetnnU-Net compatibility modeBuilt-in train/test split for both normal/nnU-Net modesrandom_statefor reproducible seedsRegion of interest (ROI) yaml dictionary intake for RTSTRUCT processingMarkdown report output post-processingcontinue_processingflag to continue autopipelinedry_runflag to only crawl the datasetMed-Imagetools, a python package offers the perfect tool to transform messy medical dataset folders to deep learning ready format in few lines of code. It not only processes DICOMs consisting of different modalities (like CT, PET, RTDOSE and RTSTRUCTS), it also transforms them into deep learning ready subject based format taking the dependencies of these modalities into consideration.IntroductionA medical dataset, typically contains multiple different types of scans for a single patient in a single study. As seen in the figure below, the different scans containing DICOM of different modalities are interdependent on each other. For making effective machine learning models, one ought to take different modalities into account.Fig.1 - Different network topology for different studies of different patientsMed-Imagetools is a unique tool, which focuses on subject based Machine learning. It crawls the dataset and makes a network by connecting different modalities present in the dataset. Based on the user defined modalities, med-imagetools, queries the graph and process the queried raw DICOMS. The processed DICOMS are saved as nrrds, which med-imagetools converts to torchio subject dataset and eventually torch dataloader for ML pipeline.Fig.2 - Med-Imagetools AutoPipeline diagramInstalling med-imagetoolspip install med-imagetools(recommended) Create new conda virtual environmentconda create -n mit
conda activate mit
pip install med-imagetools(optional) Install in development modeconda create -n mit
conda activate mit
pip install -e git+https://github.com/bhklab/med-imagetools.gitThis will install the package in editable mode, so that the installed package will update when the code is changed.Getting StartedMed-Imagetools takes two step approch to turn messy medical raw dataset to ML ready dataset.Autopipeline: Crawls the raw dataset, forms a network and performs graph query, based on the user defined modalities. The relevant DICOMS, get processed and saved as nrrdsautopipeline\
  [INPUT DIRECTORY] \
  [OUTPUT DIRECTORY] \
  --modalities [str: CT,RTSTRUCT,PT] \
  --spacing [Tuple: (int,int,int)]\
  --n_jobs [int]\
  --visualize [flag]\
  --nnunet [flag]\
  --train_size [float]\
  --random_state [int]\
  --roi_yaml_path [str]\
  --continue_processing [flag]\
  --dry_run [flag]class Dataset: This class converts processed nrrds to torchio subjects, which can be easily converted to torch datasetfrom imgtools.io import Dataset

subjects = Dataset.load_from_nrrd(output_directory, ignore_multi=True)
data_set = tio.SubjectsDataset(subjects)
data_loader = torch.utils.data.DataLoader(data_set, batch_size=4, shuffle=True, num_workers=4)Demo (Outdated as of v0.4)These google collab notebooks will introduce the main functionalities of med-imagetools. More information can be foundhereTutorial 1: Forming Dataset with med-imagetools AutopipelineTutorial 2: Machine Learning with med-imagetools and torchioContributorsThanks to the following people who have contributed to this project:@mkazmier@skim2257@fishingguy456@Vishwesh4@mnakanoContactIf you have any questions/concerns, you can reach the following contributors atsejin.kim@uhnresearch.caLicenseThis project uses the following license:Apache License 2.0