dicom-csv 0.2.1

Utils for gathering, aggregation and handling metadata from DICOM files.

Utils for gathering, aggregation and handling metadata from DICOM files.

Installation

From pip

pip install dicom-csv

or from GitHub

git clone https://github.com/neuro-ml/dicom-csv
cd dicom-csv
pip install -e .

Example join_tree

>>> 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)

>>> 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.