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.
One of our latest experiments at PYCAD was to research how we can visualize 3D data on the browser by directly using Python.
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This can help us build web apps for POCs in medical imaging.
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One great tool that we recently discovered is called stpvista.
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This is in fact a tool that allows you to integrate Pyvista plots, which is a great Python library for 3D visualization, directly inside your Streamlit web application.
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Below you can see an example of visualizing an STL file of an intraoral scan on the browser using Python and Streamlit!
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Pyvista is already a huge library for 3D visualization, so having the possibility to integrate its plots inside a Streamlit web app is just the cherry on top of the cake!
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I work daily on medical imaging problems. Here are 3 file formats that we deal with constantly.
Letβs demystify these 3 common formats: DICOM, NIFTI, and NRRD.
DICOM (Digital Imaging and Communications in Medicine)
NIFTI (Neuroimaging Informatics Technology Initiative)
NRRD (Nearly Raw Raster Data)
In summary, the choice of format often depends on the application. DICOM is great for clinical setups, NIFTI shines in research, especially neuroimaging, and NRRD offers flexibility for diverse visualization needs.
Stay informed, and choose wisely based on your specific requirements!
We Can Help You with Your Next Medical Imaging ProjectIf your company or organization is looking to build a machine learning solution for a medical imaging problem, then feel free to reach out to us at: βcontact@pycad.coβ We can help you build a full ML solution from training to deployment with affordable rates! You can check out some of the projects that we worked on here: βhttps://pycad.co/portfolio and some of our clients case studies here. |
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That's it for this week's edition, I hope you enjoyed it!
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...