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Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans
Apr 18, 2024
X-ray images play a vital role in the intraoperative processes due to their high resolution and fast imaging speed and greatly promote the subsequent segmentation, registration and reconstruction. However, over-dosed X-rays superimpose potential risks to human health to some extent. Data-driven algorithms from volume scans to X-ray images are restricted by the scarcity of paired X-ray and volume data. Existing methods are mainly realized by modelling the whole X-ray imaging procedure. In this study, we propose a learning-based approach termed CT2X-GAN to synthesize the X-ray images in an end-to-end manner using the content and style disentanglement from three different image domains. Our method decouples the anatomical structure information from CT scans and style information from unpaired real X-ray images/ digital reconstructed radiography (DRR) images via a series of decoupling encoders. Additionally, we introduce a novel consistency regularization term to improve the stylistic resemblance between synthesized X-ray images and real X-ray images. Meanwhile, we also impose a supervised process by computing the similarity of computed real DRR and synthesized DRR images. We further develop a pose attention module to fully strengthen the comprehensive information in the decoupled content code from CT scans, facilitating high-quality multi-view image synthesis in the lower 2D space. Extensive experiments were conducted on the publicly available CTSpine1K dataset and achieved 97.8350, 0.0842 and 3.0938 in terms of FID, KID and defined user-scored X-ray similarity, respectively. In comparison with 3D-aware methods ($\pi$-GAN, EG3D), CT2X-GAN is superior in improving the synthesis quality and realistic to the real X-ray images.
On graded semantics of abstract argumentation: Extension-based case
Dec 27, 2020
Based on Grossi and Modgil's recent work [1], this paper considers some issues on extension-based semantics for abstract argumentation framework (AAF, for short). First, an alternative fundamental lemma is given, which generalizes the corresponding result obtained in [1]. This lemma plays a central role in constructing some special extensions in terms of iterations of the defense function. Applying this lemma, some flaws in [1] are corrected and a number of structural properties of various extension-based semantics are given. Second, the operator so-called reduced meet modulo an ultrafilter is presented. A number of fundamental semantics for AAF, including conflict-free, admissible, complete and stable semantics, are shown to be closed under this operator. Based on this fact, we provide a concise and uniform proof method to establish the universal definability of a family of range related semantics. Thirdly, using model-theoretical tools, we characterize the class of extension-based semantics that is closed under reduced meet modulo any ultrafilter, which brings us a metatheorem concerning the universal definability of range related semantics. Finally, in addition to range related semantics, some graded variants of traditional semantics of AAF are also considered in this paper, e.g., ideal semantics, eager semantics, etc.