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

Nader Aldoj, Federico Biavati, Miriam Rutz, Florian Michallek, Sebastian Stober, Marc Dewey

Keywords: prostate gland, image processing, neural networks, magnetic resonance imaging.

Abstract:

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate (PR)
and its zones. The importance of segmenting the prostate and the prostate zones, such as the
central zone (CZ) and the peripheral zone (PZ) lies in the fact that the diagnostic guidelines
differ depending on in which zone the lesion is located. Thus, automatic prostate and prostate
zone segmentation from MR images is an important topic for many diagnostic and therapeutic
purposes. However, the prostate tissue heterogeneity and the huge varieties of prostate shapes
among patients make this task very challenging. Therefore, we propose a new neural network
named Dense U-net inspired by the state-of-the-art DenseNet and U-net to automatically
segment prostate and prostate zones. It was trained on 141 patient datasets and tested on 47
patient datasets with axial T2-weighted images in four-fold cross-validation manner. The network
can successfully segment the gland and its subsequent zones. This Dense U-net compared with
the state-of-the-art U-net achieved an average dice score for the whole prostate of 91.2± 0.8%
vs. 89.2 ± 0.8%, for CZ of 89.2± 0.2% vs. 87.4 ± 0.2%, and for PZ of 76.4± 0.2% vs. 74.0±
0.2%. The experimental results show that 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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