Optimized acquisition of prostate MRI for detection of clinically significant prostate cancer requires automatic prostate segmentation. State of the art automatic prostate segmentation is performed with convolution neural networks (CNNs). Exention of our previously developed anisotropic single plane CNN to handle multi-planar input is expected to decrease segmentation problems caused by the low inter-plane resolution of t2-weighted images. Data preprocessing includes volume alignment, intensity clipping and normalization. Comparing the performance to a similar axial network, the multi-stream model shows a visually relevant improvement in prostate segmentation.