AI-POWERED BRAIN SEGMENTATION TECHNOLOGY
Welcome, employees and researchers of all global companies. We will connect you with business experts to integrate Neurophet's technology into your business.
Deep learning engine's ultra-fast brain region structure segmentation & measurement
Deep Learining Engine
The brain MR Image based deep learning engine segments and analyzes structural information of each brain regions by anatomical characteristics, and it converts them into digital information.
Quantitatively analyzed information can be utilized to inspect neurodegeneration and abnormal structural changes in the brain.
More than 4000 normative MR data has been trained
Expertise in Neuroscience
The core engine was developed after more than 10 years of R&D by Neurophet’s neural network research team composed of engineers with PhD in brain engineering.
Automated image preprocessing eliminates brain MR image quality deviations. Segments 107 brain areas and extracts quantitative information of the brain structure in a minute.
HIGH QUALITY & SUPER FAST
Golden Standard as a brain segmentation tool. Outstanding performance than ‘FreeSurfer’
Near-zero operation failure rate and error rate
Reconstruct brain into 3D model including skull without the CT images and irregardless of ethnicity and race
The Only One in the World
INTER - SCANNER CENTER VARIABILITY
Compatibility verification of the world’s top 3 MRI devices
Perfectly compatible to the GE, Philips, Siemens’s representative 12 MRI models without special parameter setting. Compatibility with the 12 models is verified with 2000 MRI data.
Based on our core technologies, we are developing various medical application solutions for brain disease treatment / surgery guide, brain disease diagnosis, and brain image analysis.
NEUROPHET SegEngine is optimized in terms of time, speed and accuracy.
Operational 24 hour overage rate is over 50%.
Operation failure rate
Brain area error rate
Verified with three major companies's MRI machines
Significantly low error rate
Outstanding calculation accuracy