Download

Download

The viewer runs natively on your computer. It is free — no account, no licence key, no trial period.

macOS (Apple Silicon) Download for macOS
Windows (64-bit)Coming soon
Linux (AppImage)Coming soon

Version 0.2.0 · 82 MB · Updated 9 Aug 2026
SHA-256: f45385d163c2874c8e032d10180fed4c7faf1670e844ea0ffddadbdf3a650dfb

New in 0.2.0

PACS query and retrieve. Search a DICOM archive by patient, ID, accession, date range or modality, drill into a study’s series, and retrieve just the series you need. The viewer prefers C-GET, which brings images back over the same connection as the query and needs no open port; servers that only support C-MOVE are handled too, with an optional receiver that is off unless you turn it on. Opened series and archived studies can be sent back to a PACS from the save menu or the archive.

Also in this release: automatic anatomy labelling (117 structures on CT, 50 on MR) running entirely on your own machine, and a full segmentation editor with region growing, watershed and surface export.

Verifying the download

Because this build is not yet notarised by Apple, it is worth checking that the file you received is the one published here. In Terminal:

shasum -a 256 ~/Downloads/Neuroin-DICOM-Viewer-0.2.0-macOS-arm64.dmg

The output should match the SHA-256 above exactly. If it does not, delete the file and download it again.

First launch on macOS

This build is not yet notarized by Apple, so the first launch needs one extra step: open the DMG, drag Neuroin DICOM Viewer into Applications, then right-click (or Control-click) the app → Open → Open. Double-clicking the first time shows a “developer cannot be verified” message — that is macOS Gatekeeper’s standard prompt for non-notarized apps, not a problem with the download. After the first launch it opens normally.

System requirements

  • macOS — Apple Silicon (M1 or newer), macOS 14 or newer (tested on macOS 26)
  • Windows 10/11, 64-bit — coming soon
  • Linux — recent 64-bit distribution (AppImage) — coming soon
  • 8 GB RAM recommended for large CT/MR volumes; OpenGL-capable GPU
  • A further 1 GB of disk if you install the AI segmentation models

Research and educational use only — NOT FOR DIAGNOSTIC USE.