  An accurate prostate delineation and volume characterization can support the
clinical assessment of prostate cancer. A large amount of automatic prostate
segmentation tools consider exclusively the axial MRI direction in spite of the
availability as per acquisition protocols of multi-view data. Further, when
multi-view data is exploited, manual annotations and availability at test time
for all the views is commonly assumed. In this work, we explore a contrastive
approach at training time to leverage multi-view data without annotations and
provide flexibility at deployment time in the event of missing views. We
propose a triplet encoder and single decoder network based on U-Net, tU-Net
(triplet U-Net). Our proposed architecture is able to exploit non-annotated
sagittal and coronal views via contrastive learning to improve the segmentation
from a volumetric perspective. For that purpose, we introduce the concept of
inter-view similarity in the latent space. To guide the training, we combine a
dice score loss calculated with respect to the axial view and its manual
annotations together with a multi-view contrastive loss. tU-Net shows
statistical improvement in dice score coefficient (DSC) with respect to only
axial view (91.25+-0.52% compared to 86.40+-1.50%,P<.001). Sensitivity analysis
reveals the volumetric positive impact of the contrastive loss when paired with
tU-Net (2.85+-1.34% compared to 3.81+-1.88%,P<.001). Further, our approach
shows good external volumetric generalization in an in-house dataset when
tested with multi-view data (2.76+-1.89% compared to 3.92+-3.31%,P=.002),
showing the feasibility of exploiting non-annotated multi-view data through
contrastive learning whilst providing flexibility at deployment in the event of
missing views.
