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

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Over the past few years, the field of computer vision has experienced 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 learned the principal methods to segment brain tumors.

PaaS AI tumor

Info

We will train this model on the BraTS2020 dataset. We will show you how you can easily download the dataset in the notebook tutorial.

 

Requirements

 

Instructions

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

A 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.

Info

Here, using 1 GPU is sufficient.

 

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
Info

If you do not specify a version, your notebook starts with the default version of tensorflow.

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

Info

Here we recommend using 1 GPU.

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

Browse all the AI & Machine Learning guides.

Browse the rest of the OVHcloud US documentation for guides on our other products and services.

Learn more about AI & Machine Learning on the OVHcloud US website.

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.