An Introduction to Medical Imaging Analysis with Deep Learning

Introduction

It is not a mystery that medical images are a great tool in medicine. Besides being non-invasive, they are useful to diagnose, evaluate, and prevent diseases. Also, many physicians use medical images for research purposes. However, with the popularization of imaging methods, research centers are dealing with an increasing amount of data, that are costly to be manually analyzed. That`s when the medical imaging processing field is necessary. By using computational methods, engineers and computer science professionals can help physicians to diminish this bottleneck. Over the years, more researchers are joining the medical imaging processing field, however many struggle with basic concepts in the beginning of their career.

Here you find a a practical demonstration of basic principles described on our Tutorial Paper, following the workflow presented bellow:

Citation

Carmo, D., Pinheiro, G., Rodrigues, L., Abreu, T., Lotufo, R., & Rittner, L. (2023). Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide. arXiv preprint arXiv:2304.05901.

@article{carmo2023automated,
    title={Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide},
    author={Carmo, Diedre and Pinheiro, Gustavo and Rodrigues, L{\'\i}via and Abreu, Thays and Lotufo, Roberto and Rittner, Let{\'\i}cia},
    journal={arXiv preprint arXiv:2304.05901},
    year={2023}
}

How to run?

We recommend that you create a personal copy of our notebooks in Google Colaboratory for a first contact with the concepts. Feel free to use any of the code as basis for your work, however if you do please add the proper citations.

You can access the notebook in Colab in the following links:

CT Segmentation Example:

https://colab.research.google.com/drive/1xsEQb-GilPMbhecle31dsd8jEPYi7_Qa?usp=sharing

MRI Classification Example:

https://colab.research.google.com/drive/1Z37aaRDlz7MjBxstj_mrVQqJa_8NXbzr?usp=sharing

Create a copy of it in your Drive and have fun!

A copy of these notebooks is also available in the repository. If you want to run locally, you will need to setup your Python and Jupyter Notebook environment with the necessary libraries, including your NVidia GPU driver and a GPU compatible installation of PyTorch (https://pytorch.org).