-GBM Tumor Segmentation

MRIMath’s AI segmentation delivers neuro-radiologist-level precision in seconds.
Contours generate with a single click, and full review takes just 2–3 minutes—up to 30× faster than manual contouring.

Current capabilities:
GBM T1c series (Accuracy: 95%)
GBM FLAIR series (Accuracy: 92%)

-Uncertainty Quantification

Unlike black-box AI, our fully Bayesian, transparent model delivers pixel-level uncertainty maps alongside each GBM segmentation.
Know exactly where the model is confident — and where it’s not.
Designed for researchers who need traceability and trust.
(Accuracy: above 90%)

-Metastatic Brain Tumor Segmentation

Our AI model is trained on T1c MRI series from patients with metastatic cancer to the brain.
It delivers automated tumor segmentation in seconds, supporting both research workflows and clinical decision-making.
With pixel-level accuracy and consistent boundaries, our system eliminates variability and speeds up review.
Accuracy for this model exceeds 83%, making it one of the most efficient tools for metastatic brain lesion analysis.

-Organs-At-Risk Segmentation for the Brain

Our OAR segmentation model delivers fast, high-precision delineation of critical neuroanatomical structures—enabling safe, targeted treatment planning.
Built for flexibility, the model supports user-specified outputs across over 20 key structures, from optic nerves to the hippocampus.
Whether for radiotherapy planning or advanced neuroimaging research, this tool offers reliable structure-by-structure segmentation with clinical-grade consistency.
Used across academic and clinical institutions to reduce manual workload and ensure accuracy where it matters most.