U-Net Segmentation Dashboard for MRI Scan Review

Added Jun 2025 3 design docs

Outlining anatomical structures and abnormalities on MRI scans is one of the most time-consuming tasks in medical imaging, often requiring slice-by-slice manual tracing by trained specialists. Research groups and imaging teams need accessible tools that generate candidate segmentations automatically so expert time is spent verifying results rather than drawing boundaries. In this project the intern builds a Streamlit web application around a U-Net model implemented with Keras and TensorFlow. Users upload a medical image, the app runs a preprocessing pipeline using OpenCV and NumPy to normalize and resize the input, and the model produces a semantic segmentation mask identifying the target regions. The intern implements clear visualization of results, showing the original scan, the predicted mask, and a blended overlay so reviewers can immediately judge segmentation quality, and uses pandas to summarize mask statistics such as segmented area for each processed image. The build gives the intern hands-on experience with the model architecture most widely used in biomedical segmentation, teaches the preprocessing discipline that medical imagery demands, and demonstrates the ability to wrap a trained network in an interface that a clinical researcher can operate without touching code.

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