This code example shows how to use the STAPLE algorithm on a medical image dataset

Medical images contain a wealth of information that helps us understand patient health. To unlock that information, the first step is usually to segment, or trace, important structures.

Segmentation is the most important step in medical image analysis — and it’s often overlooked. …

A guide for finding and tracing pancreas on contrast-enhanced abdominal CT

Special thanks to my good friend Dr. Megan Engels, for helping me with this post.

Introduction — what is segmentation?

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…

This script will help you understand and organize your dataset of medical images

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…

How to read, write, and organize medical images

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”), …

Alexander Weston, PhD

Data scientist at Mayo Clinic. My views are entirely my own.

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