iNeural networks promise automated prostate segmentation for the development of precise and quantifiable image-based biomarkers in modern personalized oncology. Before clinical translation, however, their
stability must be ensured. In this study, we train three-dimensional Ushaped convolutional neural networks to segment prostate magnetic resonance imaging (MRI) scans and evaluate different loss formulations to
improve their performance. To evaluate generalizability and reproducibility of our networks, we compare their performance in a clinically acquired test/re-test MRI data set of 26 prostate cancer patients that was previously
not seen by the networks. We find our networks to be generalizable with good reproducibility with a mean Intersection over Union of 0.88. While initial results are promising, anatomical accuracy remains to be evaluated
in larger, multi-center data sets. To facilitate clinical applicability, we provide an easy to use toolbox online.
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Open source: https://trainee.cs.uni-bonn.de/publication/wolter-2024-on
