Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide

TL;DR

This tutorial provides an accessible overview of deep learning-based segmentation techniques for MRI and CT images, including fundamental concepts, tools, and practical examples for beginners.

Contribution

It offers a comprehensive beginner's guide with practical sample tasks, code resources, and best practices for medical image segmentation using deep learning.

Findings

Effective deep learning frameworks for segmentation

Sample tasks with public datasets and code

Guidelines for developing segmentation methods

Abstract

Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with basic concepts. This tutorial paper aims to provide an overview of the fundamental concepts of medical imaging, with a focus on Magnetic Resonance and Computerized Tomography. We will also discuss deep learning algorithms, tools, and frameworks used for segmentation tasks, and suggest best practices for method development and image analysis. Our tutorial includes sample tasks using public data, and accompanying code is available on GitHub (https://github.com/MICLab-Unicamp/Medical-ImagingTutorial). By sharing our insights gained from years of experience in the field and learning from relevant literature, we hope to assist researchers in overcoming the initial challenges they may encounter in this exciting and important area of research.