  Cardiac segmentation is in great demand for clinical practice. Due to the
enormous labor of manual delineation, unsupervised segmentation is desired. The
ill-posed optimization problem of this task is inherently challenging,
requiring well-designed constraints. In this work, we propose an unsupervised
framework for multi-class segmentation with both intensity and shape
constraints. Firstly, we extend a conventional non-convex energy function as an
intensity constraint and implement it with U-Net. For shape constraint,
synthetic images are generated from anatomical labels via image-to-image
translation, as shape supervision for the segmentation network. Moreover,
augmentation invariance is applied to facilitate the segmentation network to
learn the latent features in terms of shape. We evaluated the proposed
framework using the public datasets from MICCAI2019 MSCMR Challenge and
achieved promising results on cardiac MRIs with Dice scores of 0.5737, 0.7796,
and 0.6287 in Myo, LV, and RV, respectively.
