AI Notebooks - Tutorial - Brain tumor segmentation using U-Net

Objective

Over the past few years, the field of computer vision has experienced a significant growth. It encompasses a wide range of methods for acquiring, processing, analyzing and understanding digital images.

Among these methods, one is called image segmentation.

The purpose of this tutorial is to show you how it is possible to build and train a brain tumor segmentation model with OVHcloud AI Notebooks. You will be able to learn the concepts of medical imaging, image segmentation, model evaluation, and much more. We will use a popular convolutional neural network named U-Net.

At the end of this tutorial, you will have learnt the principal methods to segment brain tumors.

Requirements

• An AI Notebooks project created inside a Public Cloud project in your OVHcloud account
• A user for AI Notebooks
• A Kaggle account to download the dataset

OVHcloud Control Panel Access

• Direct link: Public Cloud Projects
• Navigation path: Public Cloud > Select your project

Instructions

You can launch the notebook from the OVHcloud Control Panel or via the ovhai CLI.

Direct link to the full code can be found here.

Launching a Jupyter notebook with "Tensorflow" via UI (Control Panel)

To launch your notebook from the OVHcloud Control Panel, refer to the following steps.

Code editor

Choose the Jupyterlab code editor.

Framework

In this tutorial, the tensorflow framework is used. If you use another environment, there may be some compatibility problems such as missing libraries.

Resources

Using GPUs is recommended because medical imaging is a training intensive task.

Launching a Jupyter notebook with "Tensorflow" via CLI

If you do not use our CLI yet, follow this guide to install it.

If you want to launch your notebook with the OVHcloud AI CLI, choose the jupyterlab editor and the tensorflow framework.

To access the different versions of tensorflow available, run the following command:

ovhai capabilities framework list -o yaml

You will also need to choose the number of CPUs/GPUs (<nb-cpus> or <nb-gpus>) to use in your notebook.

To launch your notebook, run the following command:

ovhai notebook run tensorflow jupyterlab \
        --name <notebook-name> \
        --framework-version <tensorflow-version> \
        --gpu <nb-gpus>

You can then reach your notebook’s URL once the notebook is running.

Accessing the notebooks

Once our AI examples repository has been cloned in your environment, find your notebook by following this path: ai-training-examples > notebooks > computer-vision > image-segmentation > tensorflow > brain-tumor-segmentation-unet > notebook_image_segmentation_unet.ipynb.

A preview of this notebook can be found on GitHub here.

Go further

There are many other tasks that exist in the computer vision field. Check our other tutorials to learn how to:

• Use Transfer Learning with ResNet50 for image classification

• Train YOLOv7 for American Sign Language recognition

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

Feedback

Please send us your questions, feedback and suggestions to improve the service:

• On the OVHcloud Discord server