  We propose and evaluate a novel method for automatically detecting clinically
significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging
(bpMRI). Prostate zones play an important role in the assessment of prostate
cancer on MRI. We hypothesize that the inclusion of zonal information can
improve the performance of a deep learning based csPCa lesion detection model.
However, segmentation of prostate zones is challenging and therefore
deterministic models are inaccurate. Hence, we investigated probabilistic zonal
segmentation. Our baseline detection model is a 2DUNet trained to produce a
csPCa heatmap followed by a 3D detector. We experimented with the integration
of zonal prior information by fusing the output of an anisotropic 3DUNet
trained to produce either a deterministic or probabilistic map for each
prostate zone. We also investigate the effect of early or late fusion on csPCa
detection. All methods were trained and tested on 848 bpMRI. The results show
that fusing zonal prior knowledge improves the baseline detection model with a
preference for probabilistic over deterministic zonal segmentation.
