Special thanks to my good friend Dr. Megan Engels, for helping me with this post.
CT scans contain a wealth of information that can help us understand a patient’s health. As data scientists, our role is to extract the information so it can be measured, or quantified.
The first step to analyzing CT or MRI scans is usually segmentation. By this, I mean tracing — segmenting — important structures from background. From segmentations, we extract important features like organ volume, surface area, brightness, and texture patterns that tell us about various aspects of the disease.
This article is a follow-up to my previous introduction to DICOM files. Special thanks to my good friend Dr. Gian Marco Conte for helping write this.
As a brief recap, DICOM files are the primary format for storing medical images. All clinical algorithms must be able to read and write DICOM. But these files can be challenging to organize. DICOM files have information associated with the image saved in a header, which can be extensive. Files are structured in 4 tiers:
In this tutorial, I’ll share some python code that reads a set of DICOM files…
DICOM is the primary file format for storing and transferring medical images in a hospital’s database.
There are other file formats for storing images. Besides DICOM, you may also see medical images saved in the NIFTI format (file suffix “.nii”), PNG or JPEG format, or even Python file objects like NumPy arrays.
So why use DICOM? Other file formats may be more convenient, but in clinical practice, everything uses the DICOM format. As my projects get more advanced, I’ve often found myself rewriting code to read and write DICOM files directly. …
Data scientist at Mayo Clinic. My views are entirely my own.