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Abstract #2744

Clinical Implementation of Novel PACS-based Deep Learning Glioma Segmentation Algorithm

Sara Merkaj1, Khaled Bousabarah2, Lin MingDe1, Andrej Pala3, Gabriel Cassinelli Petersen1, Leon Jekel1, Ryan Bahar1, Niklas Tillmanns4, Ajay Malhotra1, Malte Westerhoff2, and Mariam Aboian1
1Yale School of Medicine, New Haven, CT, United States, 2Visage Imaging, Berlin, Germany, 3Ulm University, Ulm, Germany, 4University of Düsseldorf, Düsseldorf, Germany


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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