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Abstract #2333

Deep Learning Segmentation of Rectal Cancer on MRI

Endre Grøvik1,2, Darvin Yi3, Franziska Knuth4, Sebastian Meltzer5, Anne Negård5, and Kathrine Røe Redalen4
1Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway, 2Faculty of Health Sciences, University of South-Eastern Norway, Drammen, Norway, 3University of Illinois at Chicago, Chicago, IL, United States, 4Norwegian University of Science and Technology, Trondheim, Norway, 5Akershus University Hospital, Lørenskog, Norway

Treatment of rectal cancer often requires repeated identification of the tumor volume by means of manual delineation by expert radiologists or oncologists. This is a tedious and time-consuming task, particularly with the growing use of multi-sequence 3D imaging. In this work, we have implemented a deep neural network for automatic detection and segmentation of rectal cancer. Our model demonstrates high detection and segmentation performance, equivalent to that of an expert reader, thus illustrating the potential use of deep learning-based segmentation in a clinically relevant setting.

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