  Various convolutional neural network (CNN) based concepts have been
introduced for the prostate's automatic segmentation and its coarse subdivision
into transition zone (TZ) and peripheral zone (PZ). However, when targeting a
fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the
anterior fibromuscular stroma (AFS), the task becomes more challenging and has
not yet been solved at the level of human performance. One reason might be the
insufficient amount of labeled data for supervised training. Therefore, we
propose to apply a semi-supervised learning (SSL) technique named
uncertainty-aware temporal self-learning (UATS) to overcome the expensive and
time-consuming manual ground truth labeling. We combine the SSL techniques
temporal ensembling and uncertainty-guided self-learning to benefit from
unlabeled images, which are often readily available. Our method significantly
outperforms the supervised baseline and obtained a Dice coefficient (DC) of up
to 78.9% , 87.3%, 75.3%, 50.6% for TZ, PZ, DPU and AFS, respectively. The
obtained results are in the range of human inter-rater performance for all
structures. Moreover, we investigate the method's robustness against noise and
demonstrate the generalization capability for varying ratios of labeled data
and on other challenging tasks, namely the hippocampus and skin lesion
segmentation. UATS achieved superiority segmentation quality compared to the
supervised baseline, particularly for minimal amounts of labeled data.
