Tumor segmentation is a laborious process, which impedes the progress of data production for development of classification/prediction algorithms. We present a novel PACS-based workflow for deep learning-based auto-segmentation of gliomas that allows generation of annotated images during clinical workflow. We developed a U-Net auto-segmentation algorithm natively imbedded in PACS and trained on BraTS dataset. Subsequent retraining on tertiary hospital dataset was performed and generation of new segmentations, allowing labeling of 440 gliomas in a three-months period. This novel approach for annotated data generation allows real-time building of large, labeled datasets by experts in the field.
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