--- title: "AI Notebooks - Tutorial - Brain tumor segmentation using U-Net" description: "Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging" url: https://docs.ovhcloud.com/en/guides/public-cloud/ai-machine-learning/ai-notebooks-tuto-image-segmentation-unet-tumors lang: en lastUpdated: 2023-05-11 --- # 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**. :::info We will train this model on the _[BraTS2020 dataset](http://braintumorsegmentation.org/)_. We will show you how you can easily download the dataset in the notebook tutorial. ::: ## Requirements - An AI Notebooks project created inside a [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account - A user for AI Notebooks - A [Kaggle](https://www.kaggle.com/) 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](/en/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md) . Direct link to the full code can be found [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/computer-vision/image-segmentation/tensorflow/brain-tumor-segmentation-unet/notebook_image_segmentation_unet.ipynb). ### 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](/en/guides/public-cloud/ai-machine-learning/ai-cli-install-client.md) 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: ```console 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 (`` or ``) to use in your notebook. :::info Here we recommend using `1 GPU`. ::: To launch your notebook, run the following command: ```console ovhai notebook run tensorflow jupyterlab \ --name \ --framework-version \ --gpu ``` You can then reach your notebook’s URL once the notebook is running. ### Accessing the notebooks Once our [AI examples repository](https://github.com/ovh/ai-training-examples/) 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](https://github.com/ovh/ai-training-examples/blob/main/notebooks/computer-vision/image-segmentation/tensorflow/brain-tumor-segmentation-unet/notebook_image_segmentation_unet.ipynb). ## 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](/en/guides/public-cloud/ai-machine-learning/ai-notebooks-transfer-learning-resnet.md) - [Train YOLOv7 for American Sign Language recognition](/en/guides/public-cloud/ai-machine-learning/ai-notebooks-yolov7-sign-language.md) If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) 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](https://discord.gg/ovhcloud)