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Why Medical Imaging Is Moving to the Browser

There’s a clear shift happening in medical imaging: more and more platforms are going web-first. And it’s not just for design reasons. When imaging tools run in the browser, doctors don’t have to install heavy software, deal with updates, or call IT, they just log in and start working. That alone removes a huge barrier for radiologists, surgeons, dentists, and remote teams.

On the technical side, web apps give companies full control: you can scale GPUs when a segmentation job is heavy, push fixes instantly, and keep everyone on the same version. And with AI becoming a core part of imaging workflows (segmentation, AI reporting, study summarization, data sharing), the browser is simply the best place to orchestrate all of it. Even hardware-focused companies are now adding web-based planning tools so clinicians can do implant or surgery planning online, from anywhere.

So when we say “the future of medical imaging is in the browser,” it’s not hype, it’s just the most practical, scalable, and AI-ready path.

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Top GitHub Repos for Medical Imaging

If you work in medical imaging or AI, here are 10 GitHub repositories that you’ll find incredibly useful. I’ve used most of them in my own projects — they make everything from preprocessing to visualization much faster and easier.

Let’s go through them 👇

1. ​nnU-Net – The gold standard for biomedical image segmentation. It automatically adapts to your dataset and remains one of the strongest baselines for 2D and 3D segmentation tasks.
2. ​TotalSegmentator – A powerful open-source model for whole-body CT and MRI segmentation. It covers over 100 anatomical structures and is widely used for research and automation.
3. ​SimpleITK – A simplified, multi-language interface to ITK that allows you to prototype image processing workflows quickly — perfect for research and experimentation.
4. ​3D Slicer – A complete desktop platform for visualization, segmentation, and extension development. It’s the foundation of many custom imaging tools built today.
5. ​pydicom – The essential Python library for reading, writing, and modifying DICOM files. If you work with medical data, this is a must-have.
6. ​Orthanc – A lightweight, open-source DICOM server (PACS) that’s perfect for hospitals, startups, and research teams looking for flexibility and control.
7. ​Project MONAI – The deep learning framework built on PyTorch for medical imaging. It provides standardized workflows for segmentation, classification, and registration.
8. ​ITK – The backbone of medical image processing — offering robust algorithms for registration, filtering, and segmentation.
9. ​cornerstone3D – A JavaScript toolkit for building browser-based medical imaging viewers. Ideal for web apps and cloud-based visualization.
10. ​TorchIO – A great tool for 3D medical image I/O, preprocessing, and data augmentation — built specifically for PyTorch users.

Each of these open-source projects represents years of work by the medical imaging community. Whether you’re developing your own DICOM viewer, training AI models, or just exploring how medical data is processed, these repos are the perfect place to start.

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