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Abstract #1711

Automatic prostate and prostate zones segmentation of magnetic resonance images using convolutional neural networks

Nader Aldoj1, Federico Biavati1, Miriam Rutz1, Sebastian Stober2, and Marc Dewey1

1Charite, Berlin, Germany, 2University of potsdam, Berlin, Germany

The purpose was to develop a fully automatic and accurate tool for prostate and prostate zone segmentation using T2-weighted MRI. Thus, we developed a new neural network named Dense U-Net which was trained on 143 patient datasets and tested on 45 patient datasets. This Dense U-Net compared with the state-of-the-art U-Net achieved an average dice score for the whole prostate of 89.4±0.8% vs. 88.4±0.8%, for the central zone of 83±0.2% vs. 83±0.2%, and for the peripheral zone of 76.9±0.2% vs. 74.6±0.2%, respectively. In conclusion, the developed Dense U-Net was more accurate than the state-of-the-art U-Net for prostate and prostate zone segmentation.

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