2024/06/26 by Abril Corona-Figueroa, Hubert P. H. Shum, Corona-Figueroa, Abril +3
Computer Science · #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Generative grammar #Image (mathematics) #Image and Video Processing (eess.IV) #Image translation #Translation (biology) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.18422
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/06/26 · openalex created_date 2024/06/28 · openalex updated_date 2026/07/28
This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.