π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
How to Visualize 3D Medical Data
Hi Reader,,
Welcome to the PYCAD newsletter, where every week you receive doses of machine learning and computer vision techniques and tools to help you learn how to build AI solutions to empower the most vulnerable members of our society, patients.
Top Medical Imaging Repos of the Week
βMedical Imaging Segmentation Toolkit: a simple, fully automated 3D medical imaging segmentation framework for PyTorch and TensorFlow.
βDIPY: the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
βNiiVue: a WebGL2 based medical image viewer. Supports over 30 formats of volumes and meshes.
βSelf-Prompting Large Vision Models for Few-Shot Medical Image Segmentation official repo.
βKaapana: an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis.
ML Deep Dive: 3D Visualization for Medical Imaging
Lately I experimented a lot with 3D visualization for medical imaging applications.
Here are 6 tools to help you visualize NIFTI files in Python.
- Matplotlib.
- Pyvista.
- iPyVolume.
- k3d.
- vedo.
- vtk.
Each one of these can be used in different scenarios:
iPyVolume for example can be used to visualize NIFTI files directly in your jupyter or colab notebook.
VTK can be used to build powerful visualizations from the ground up where you control every little detail.
k3d is simple and very pythonic. It has very similar syntax to matplotlib.
But my current favorite one is definitely vedo.
It uses VTK as a backend but it tremendously simplifies the code for visualization.
With a few lines of code you can build even interactive visualizations like the one shared below of an embyo and itβs very fast!
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Tweet of the Day
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Meme of the Day π
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That's it for this week's edition, I hope you enjoyed it!
Machine Learning for Medical Imaging
by Nour Islam Mokhtari from pycad.co
π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hi Reader,, Welcome to the PYCAD newsletter, where every week you receive doses of machine learning and computer vision techniques and tools to help you learn how to build AI solutions to empower the most vulnerable members of our society, patients. TotalSegmentator : Whole Body Segmentation at your Fingertips This free tool available online can do full body segmentation, it's called TotalSegmentator. I have already mentioned this tool in a previous edition of the newsletter, but in this...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β A Medical Imaging Expert Told Me This Recently I saw a post on LinkedIn where a medical imaging expert showcased his work of segmenting the lungs and its bronchial trees. You can...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β How we helped accelerate inference time for a client's AI product Below is a screenshot of a benchmark we did for a client of ours. The goal was to accelerate inference time. This...