Downloading and Preprocessing Medical Images in Bulk

DICOM to Numpy with Python

If you are interested in creating your own medical image data set for a machine learning project, this post is for you. You may also be interested in this post if you work with publicly-available medical imaging datasets and would like some further insight into how these datasets are created.

In this post, you will learn:

*  The basics of DICOM, the file format in which medical images are stored in health systems;

*  How to use Python to bulk download tens of thousands of medical images as DICOMs from health system storage;

*  How to clean the DICOMs and convert them into NumPy arrays using an end-to-end Python pipeline that I developed while preparing the RAD-ChestCT data set of 36,316 chest computed tomography volumes, one of the largest volumetric medical imaging datasets in the world.

This post is based in part on an appendix to my recent paper, “Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes” (published in the journal Medical Image Analysis and also available on arXiv). If you find this post helpful in your own research, please consider citing our paper. The code for this post is publicly available on GitHub: rachellea/ct-volume-preprocessing.

python radiology data-analysis numpy data-science

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