Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner’s guide
arXiv:2304.05901v1 [eess.IV] 12 Apr 2023
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 (this https URL). 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.
Full paper here: https://doi.org/10.48550/arXiv.2304.05901
- Authors: Diedre Carmo, Gustavo Pinheiro, Lívia Rodrigues, Thays Abreu, Roberto Lotufo, Letícia Rittner
- How to cite: CARMO, Diedre et al. Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide. arXiv preprint arXiv:2304.05901, 2023.
- Published: 12 April 2023