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- Read more about COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION
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We investigate the impacts of objective functions on the performance of deep-learning-based prostate magnetic resonance image segmentation. To this end, we first develop a baseline convolutional neural network (BCNN) for the prostate image segmentation, which consists of encoding, bridge, decoding, and classification modules. In the BCNN, we use 3D convolutional layers to consider volumetric information. Also, we adopt the residual feature forwarding and intermediate feature propagation techniques to make the BCNN reliably trainable for various objective functions.
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