Get the AI Segmentation + DICOM Viewer Integration Guide
Integrating AI into a neuroimaging viewer is not just about running a model. You need the right architecture, output format, metadata, and viewer behavior so segmentations can be reviewed, overlaid, measured, and used inside the clinical or research workflow.
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Who am I?
I’m Nour Islam Mokhtari, founder of PYCAD. We help healthcare companies, medical device teams, and imaging startups build custom DICOM viewers, AI imaging workflows, and web-based medical imaging platforms.
At PYCAD, we work with both server-side and client-side viewer architectures, so this guide is based on real implementation tradeoffs, not generic theory.
What will you learn?
• Why the AI model should usually stay outside the DICOM viewer
• How to connect an AI segmentation pipeline to a neuroimaging viewer
• Which output formats work best: NIfTI, DICOM SEG, RTSTRUCT, or meshes
• What metadata your segmentation outputs need so the viewer can display them correctly
• How overlays, opacity controls, label maps, measurements, and 3D views should behave
• Common mistakes teams make when integrating AI outputs into viewers
• A simple checklist to use before building the workflow