Metadata-Version: 2.1
Name: med-imagetools
Version: 0.4.1
Summary: Transparent and reproducible image processing pipelines in Python.
Home-page: https://github.com/bhklab/med-imagetools
Author: Michal Kazmierski, Sejin Kim, Kevin Qu, Vishwesh Ramanathan, Benjamin Haibe-Kains
Author-email: benjamin.haibe.kains@utoronto.ca
License: UNKNOWN
Description: # Med-Imagetools: Transparent and Reproducible Medical Image Processing Pipelines in Python
        ![GitHub repo size](https://img.shields.io/github/repo-size/bhklab/med-imagetools)
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        ### Latest Updates (v0.4.1) - June 24th, 2022
        New features include:
        * AutoPipeline CLI
        * nnU-Net compatibility mode (--nnunet)
        * Built-in train/test split for both normal/nnU-Net modes
        * Random seed for reproducible seeds
        * Region of interest (ROI) yaml dictionary intake for RTSTRUCT processing
        
        Med-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.  
        
        ## Introduction
        A 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.
        
        <a href="url"><img src="https://github.com/bhklab/med-imagetools/blob/master/images/graph.png" align="center" width="480" ><figcaption>Fig.1 - Different network topology for different studies of different patients</figcaption></a>  
        
        Med-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.
        
        <a href="url"><img src="https://github.com/bhklab/med-imagetools/blob/master/images/methodology.png" align="center" width="500"><figcaption>Fig.2 - Med-Imagetools start to end pipeline</figcaption></a>  
        
        ## Installing med-imagetools
        
        ```
        pip install med-imagetools
        ```
        ### (recommended) Create new conda virtual environment
        ```
        conda create -n mit
        conda activate mit
        pip install med-imagetools
        ```
        
        ### (optional) Install in development mode
        
        ```
        conda create -n mit
        conda activate mit
        pip install -e git+https://github.com/bhklab/med-imagetools.git
        ```
        This will install the package in editable mode, so that the installed package will update when the code is changed.
        
        ## Getting Started
        Med-Imagetools takes two step approch to turn messy medical raw dataset to ML ready dataset.  
        1. ***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 nrrds
            ```
            autopipeline\
              [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]
            ```
        2. ***class Dataset***: This class converts processed nrrds to torchio subjects, which can be easily converted to torch dataset
            ```
            from 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 (Incompatible with v0.4)
        These google collab notebooks will introduce the main functionalities of med-imagetools. More information can be found [here](https://github.com/bhklab/med-imagetools/blob/master/examples/README.md)
        #### Tutorial 1: Forming Dataset with med-imagetools Autopipeline
        
        [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/skim2257/tcia_samples/blob/main/notebooks/Tutorial_1_Forming_Dataset_with_Med_Imagetools.ipynb)
        
        #### Tutorial 2: Machine Learning with med-imagetools and torchio
        
        [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/skim2257/tcia_samples/blob/main/notebooks/Tutorial_2_Machine_Learning_with_Med_Imagetools_and_torchio.ipynb)
        
        ## Contributors
        
        Thanks to the following people who have contributed to this project:
        
        * [@mkazmier](https://github.com/mkazmier)
        * [@skim2257](https://github.com/skim2257)
        * [@fishingguy456](https://github.com/fishingguy456)
        * [@Vishwesh4](https://github.com/Vishwesh4)
        * [@mnakano](https://github.com/mnakano)
        
        ## Contact
        
        If you have any questions/concerns, you can reach the following contributors at sejin.kim@uhnresearch.ca
        
        ## License
        
        This project uses the following license: [Apache License 2.0](http://www.apache.org/licenses/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Development Status :: 2 - Pre-Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: debug
