Medical Image Annotation and Segmentation Workspace for Research Teams

Added Jun 2025 3 design docs

Progress in medical imaging AI is bottlenecked by annotation: expert time is scarce, labeling a single scan can take an hour, and inconsistent annotations quietly poison downstream research. Tools that let AI propose segmentations for experts to verify multiply what a research team can produce with the same clinical hours. The intern builds a web workspace where generative AI accelerates that loop. Uploaded medical images are preprocessed with OpenCV, and the AI proposes annotations and segmentation masks that radiologists and researchers refine rather than draw from scratch. The React frontend provides precise editing tools with layer overlays, while the Python backend manages the model pipeline, collaborative editing sessions, and full version control so every change to an annotation is attributable and reversible. Finished datasets export in standard formats ready for model training and publication, and secure authentication restricts access to authorized clinical and research users. The intern gains experience at the intersection of computer vision and healthcare research: human-in-the-loop AI design, image pipeline engineering, and the provenance and access controls that medical data genuinely demands.

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