  Background and Objective: Accurate and reliable segmentation of the prostate
gland in MR images can support the clinical assessment of prostate cancer, as
well as the planning and monitoring of focal and loco-regional therapeutic
interventions. Despite the availability of multi-planar MR scans due to
standardized protocols, the majority of segmentation approaches presented in
the literature consider the axial scans only. Methods: We propose an
anisotropic 3D multi-stream CNN architecture, which processes additional scan
directions to produce a higher-resolution isotropic prostate segmentation. We
investigate two variants of our architecture, which work on two (dual-plane)
and three (triple-plane) image orientations, respectively. We compare them with
the standard baseline (single-plane) used in literature, i.e., plain axial
segmentation. To realize a fair comparison, we employ a hyperparameter
optimization strategy to select optimal configurations for the individual
approaches. Results: Training and evaluation on two datasets spanning multiple
sites obtain statistical significant improvement over the plain axial
segmentation ($p<0.05$ on the Dice similarity coefficient). The improvement can
be observed especially at the base ($0.898$ single-plane vs. $0.906$
triple-plane) and apex ($0.888$ single-plane vs. $0.901$ dual-plane).
Conclusion: This study indicates that models employing two or three scan
directions are superior to plain axial segmentation. The knowledge of precise
boundaries of the prostate is crucial for the conservation of risk structures.
Thus, the proposed models have the potential to improve the outcome of prostate
cancer diagnosis and therapies.
