Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
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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 reveals the volumetric positive impact of the contrastive loss when paired with tU-Net (2.85+-1.34 shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2.76+-1.89 showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.
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