NeuroTrace: Turning Brain MRI Segmentation into an Evidence-Driven Research Workflow
This is a submission for the MLH x DEV Writing Challenge What I Built NeuroTrace — From Segmentation to Evidence. Brain MRI segmentation is an important part of computational neuroimaging research. Deep learning models can identify potential tumor regions, but obtaining a segmentation mask is only one part of the workflow. Researchers still need to inspect the results, visualize the segmentation…
NeuroTrace is a research platform that streamlines the brain MRI segmentation analysis and research workflow. It utilizes a pretrained 3D U-Net model to segment brain tumors from FLAIR MRI volumes and offers interactive visualization tools for 2D and 3D examination. The platform also provides deterministic quantitative analysis, including tumor voxel count, physical volume, bounding box, centroids, brain volume fraction, and slice-wise distribution.
Quality checks are performed for segmentation accuracy, boundary overflow, connected components, and signal-to-noise estimation. An agentic AI assistant, powered by Google's Gemma 4, assists researchers in interpreting results, answering questions, and generating research reports. The reports include study metadata, model information, quantitative measurements, technical quality checks, visualizations, and AI-generated commentary. Raw MRI data remains on the local machine, ensuring privacy during the AI assistant's operation.
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