Precision MRI Segmentation for Predictive Health AI
Partnered with a leading US-based diagnostic AI firm to create a 'Ground Truth' dataset of 50,000+ abdominal MRI scans, enabling the development of a breakthrough metabolic health algorithm.
The Challenge
NeuroVision faced a critical bottleneck in their FDA approval pipeline:
They possessed a massive dataset of raw MRI scans but lacked the labeled training data required for their model. Internal radiologists were too expensive and time-constrained to perform manual pixel-level segmentation. Previous attempts with generic crowdsourcing failed to accurately distinguish between complex soft tissues (visceral vs. subcutaneous fat) and muscle groups.
Our Solution
Peakpoint Services deployed a specialized 'Anatomy Pod' operating within a SOC 2 compliant clean-room:
Recruited a team of 20 university-educated analysts with backgrounds in human biology and anatomy. Implemented a 3-tier Quality Assurance (QA) workflow with 100% review of all segmentation masks. Utilized proprietary annotation tools for precise organ and fat tissue delineation. Established a secure, encrypted data tunnel to ensure zero data egress (HIPAA compliance).",
"The difference with Peakpoint is their domain expertise. We didn't have to teach them anatomy; they already knew it. Their ability to deliver radiologist-level accuracy at scale accelerated our model training by 12 months and was pivotal in our FDA validation process."
— Dr. Aris Thorne, Chief Data Scientist at NeuroVision AI
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