Deep variational segmentation of topology-constrained object sets, with correlated uncertainty models, for robustness to degradations

2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP(2023)

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Abstract
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.
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Key words
Segmentation,DNN,topology constraints,uncertainty with correlations,out-of-distribution
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