Deep Variational Segmentation of Topology-Constrained Object Sets, with Correlated Uncertainty Models, for Robustness to Degradations

Some key applications in medical image segmentation involve object complexes having specific topologies, but typical deep neural networks (DNNs) ignore such topologies. We propose a novel DNN framework to model topology-constrained object boundaries, incorporating both individual-object and multi-object topology constraints. Unlike typical DNNs, our topology-constrained DNN makes the learning significantly more robust to out-of-distribution images. Moreover, our DNN combines variational modeling in latent-space with uncertainty modeling of boundary points along with inter-point correlations. Results on publicly available datasets show our framework to outperform existing methods.