Metadata-Version: 2.1
Name: dicom_csv
Version: 0.2.1
Summary: Utils for gathering, aggregation and handling metadata from DICOM files.
Home-page: https://github.com/neuro-ml/dicom-csv
License: MIT
Download-URL: https://github.com/neuro-ml/dicom-csv/v0.2.1.tar.gz
Description: Utils for gathering, aggregation and handling metadata from DICOM files.
        
        # Installation
        
        From pip
        ```
        pip install dicom-csv
        ```
        
        or from GitHub
        
        ```bash
        git clone https://github.com/neuro-ml/dicom-csv
        cd dicom-csv
        pip install -e .
        ```
        
        # Example `join_tree`
        
        ```python
        >>> from dicom_csv import join_tree
        >>> folder = '/path/to/folder/'
        >>> meta = join_tree(folder, verbose=2)
        >>> meta.head(3)
        ```
        | AccessionNumber | AcquisitionDate |  ...  | WindowCenter | WindowWidth |
        | -------------: | -------------:   | :---: | --------:    | :---------: |
        |000002621237 	 |20200922          |...    |-500.0        |1500.0       |
        |000002621237 	 |20200922          |...    |-40.0         |400.0        |
        |000002621237 	 |20200922          |...    |-500.0        |1500.0       |
        3 rows x 155 columns
        
        
        # Example load 3D image
        from a series of dicom files (each containing 2D image)
        
        ```python
        >>> from dicom_csv import join_tree, order_series, stack_images
        >>> from pydicom import dcmread
        >>> from pathlib import Path
        >>>
        >>> # 1. Collect metadata from all dicom files
        >>> folder = Path('/path/to/folder/')
        >>> meta = join_tree(folder, verbose=2)
        >>>
        >>> # 2. Select series to load
        >>> uid = '...' # unique identifier of a series you want to load,
        >>>             # you could list them by `meta.SeriesInstanceUID.unique()`
        >>> series = meta.query("SeriesInstanceUID==@uid")
        >>>
        >>> # 3. Read files & combine them into a single volume
        >>> images2d = [dcmread(folder / row[1].PathToFolder / row[1].FileName) for row in series.iterrows()] 
        >>> image3d = stack_images(order_series(images2d))
        ```
        
        # Documentation
        
        You can find the documentation [here](https://dicom-csv.readthedocs.io/en/latest/index.html).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
