The present repository serves as an implementation of diverse 3D deep learning segmentation architectures for the purpose of detecting lesions within the prostate.
In this study, a collection of datasets obtained from eight clinical centers located in various regions of Europe and three MR vendors (General Electric, Philips, and Siemens) were employed to train distinct models for analysis. Each patient consists of T2-Weighted, ADC and DWI images while the preprocessing of the data performed utilizing te nnU-Net [1] to segment Prostate's Whole gland.

This work is supported by the ProCancer-I project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 952159. It reflects only the author’s view. The Commission is not responsible for any use that may be made of the information it contains.

[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z

Zaridis Dimitrios. [email protected]

Mylona Eugenia [email protected]

Kalantzopoulos Charalampos [email protected]

Nikolaos Tachos [email protected]