  In this paper, we proposed a novel architecture of convolutional neural
network (CNN), namely Z-net, for segmenting prostate from magnetic resonance
images (MRIs). In the proposed Z-net, 5 pairs of Z-block and decoder Z-block
with different sizes and numbers of feature maps were assembled in a way
similar to that of U-net. The proposed architecture can capture more
multi-level features by using concatenation and dense connection. A total of 45
training images were used to train the proposed Z-net and the evaluations were
conducted qualitatively on 5 validation images and quantitatively on 30 testing
images. In addition, three approaches including pad and cut, 2D resize, and 3D
resize for uniforming the size of samples were evaluated and compared. The
experimental results demonstrated that the 2D resize is the most suitable
approach for the proposed Z-net. Compared to the other two classical CNN
architectures, the proposed method was observed with superior performance for
segmenting prostate.
