Brain Segmentation
Fully automated volumetric segmentation of the whole brain from T1-weighted MRI — delivering atlas-labelled region maps and quantitative volume reports in a single click.
Module Overview
Deep-learning morphometry, delivered at the workstation
The Brain Segmentation module applies a convolutional neural network trained on thousands of manually annotated MRI volumes to structural T1 scans, automatically identifying and measuring every major brain region. No manual seed-point placement or parameter tuning required — the model self-adapts to scanner field strength and acquisition protocol.
Results are delivered as colour-coded overlay images, a detailed volumetric table, and a one-page clinical summary PDF — all within the same desktop session. Patient data never leaves the institution.
Output artefacts
Key Capabilities
What the module delivers
Atlas-based parcellation
Automated segmentation of 90+ cortical and subcortical regions using AAL, Desikan-Killiany, Harvard-Oxford, and Brodmann frameworks.
Normative comparison
Volumetric measurements are benchmarked against age-matched reference ranges, with deviations flagged in the clinical report.
Multi-sequence compatibility
Accepts T1-weighted MPRAGE, MP2RAGE, and FLASH acquisitions from both 3T and 7T systems with no manual protocol configuration.
Sub-mm precision
Full hemisphere segmentation completed in under 10 minutes on CPU, under 3 minutes on GPU — at sub-millimetre resolution.
Technical Specifications
v2 moduleAI Automation
All segmentation is driven by a CNN trained on thousands of manually annotated MRI volumes. The model self-adapts to scanner field strength and protocol, making it suitable for multi-site studies and routine clinical workflows alike — no calibration scans or site-specific tuning required.
Get Started
Interested in the MRI Analysis Service?
Speak with our team about a pilot programme or custom integration.