Deep Variational Segmentation of Topology-Constrained Object Sets, with Correlated Uncertainty Models, for Robustness to Degradations | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Some key applications in medical image segmentation involve object complexes having specific topologies, but typical deep neural networks (DNNs) ignore such topologies. W...Show More

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.
Date of Conference: 08-11 October 2023
Date Added to IEEE Xplore: 11 September 2023
ISBN Information:
Conference Location: Kuala Lumpur, Malaysia

References

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