Prostate zonal segmentation is an important step for automated PCa diagnosis, MRI-guidedradiotherapy and focal treatment planning. Here we proposed a multi-channel U-Net for automatic prostate zonal segmentation, able to include multiple MRI sequences. Using a small, multicenter, multiparametric MRI dataset, we investigated its robustness towards the acquisition protocol and whether additional imaging sequences improve segmentation performance. Our results show that T2-weighted imaging alone is sufficient for successful prostate zonal segmentation. Despite using a small multicenter dataset, the models were robust towards the acquisition protocol and the performance was comparable to that obtained with larger datasets from a single institute.
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