  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.
