Micro-CT Synthesis and Inner Ear Super Resolution via Bayesian Generative Adversarial Networks
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Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner. However, such image pairs are often not available in clinical practice. In this paper, we address super resolution problem in a real-world scenario using unpaired data and synthesize linearly eight times higher resolved Micro-CT images of temporal bone structure, which is embedded in the inner ear. We explore cycle-consistency generative adversarial networks for super-resolution task and equip the translation approach with Bayesian inference. We further introduce Hu Moment the evaluation metric to quantify the structure of the temporal bone. We evaluate our method on a public inner ear CT dataset and have seen both visual and quantitative improvement over state-of-the-art deep-learning based methods. In addition, we perform a multi-rater visual evaluation experiment and find that trained experts consistently rate the proposed method highest quality scores among all methods. Implementing our approach as an end-to-end learning task, we are able to quantify uncertainty in the unpaired translation tasks and find that the uncertainty mask can provide structural information of the temporal bone.
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