About Ocora
Problem
Glioma reporting still relies on manual, slice-by-slice assessment of multi-sequence MRI. Delineating whole tumour, core and enhancing components can take up to hours per case, is highly reader-dependent, and is difficult to reproduce across centres. At the same time, treatment decisions increasingly depend on quantitative metrics (subregion volumes, midline shift, spatial relationships) and longitudinal change, which are rarely computed in a consistent, standardised way in routine practice. As a result, radiologists carry a heavy cognitive and time burden, and neuro-oncology teams often lack fast, objective and comparable tumour subregion data at the moment they need it most.
Value Proposition
Ocora is an AI pipeline that runs on top of routine multiparametric brain MRI to automatically segment glioma subregions and reconstruct a patient-specific 3D tumour model in situ. Within minutes, it generates PACS-ready overlays and a structured report with key quantitative outputs and planning-relevant metrics. Radiologists remain in the loop as they can quickly review, adjust and approve the AI masks, while Ocora standardises measurements for response assessment, follow-up comparison and tumour boards. The goal is to turn a time-consuming manual task into a fast, automated, and clinically interpretable workflow that supports both diagnostic confidence and treatment planning
Our Solution
