Last updated: 2024-01-17 (with bmed365 kernel), A. Lundervold
This lab will give an example-based introduction to multiparametric Magnetic Resonance Imaging (mpMRI) of brain tumor (glioma), using Python and toolkits for computational medical imaging and analysis.
The following notebook is essentially a copy of the multi-class semantic segmentation notebook 10d_tutorial_multiclass_segmentation.ipynb from fastMONAI (developed by Satheshkumar Kaliyugarasan and Alexander S. Lundervold at MMIV), and also incoporating parts of the extended notebook in https://github.com/MMIV-ML/fastMONAI/tree/master/presentations/MMIV-1022. We will not have time for a detailed introduction to MONAI. Please consult the documentation: https://monai.io and also see their tutorials.
| Notebook | 1-Click Notebook |
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| Lab1-1-BRATS-3D-fastMONAI-extended.ipynb Multi-class semantic segmentation of a glioma from mpMRI recordings If on Colab: Remember to attach a GPU to your Colab Runtime: 1. From the Runtime menu select Change Runtime Type 2. Choose GPU (T4 GPU) from the drop-down menu 3. Click 'SAVE' (~3 min to install fastMONAI with GPU attached) |
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Some additional (optional) example-based introductions to (bio)medical imaging is in Lab-optional-imganing
| Notebook | 1-Click Notebook |
|---|---|
| 01-imaging-intro.ipynb Illustration of basic concepts and methods in imaging |
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| 02-mri-intro.ipynb Introduction to Magnetic Resonance Imaging |
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| 03-imc-intro.ipynb Introduction to Imaging Mass Cytometry |
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Spend some time playing around with the provided examples. You'll find some questions for you to investigate in the notebooks. If you're already familiar with medical imaging and image analysis you can try your hand at more advanced examples, or, even better, help out other less experienced team members.
As Jupyter Notebook is quite new to many of you, you may want to skim through some tutorials. Here are two (also linked under "Getting Started" at MittUiB):