Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations

• URL: http://arxiv.org/abs/2309.08747v2
• Date: Tue, 19 Sep 2023 15:48:49 GMT
• Title: Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
• Authors: Reuben Dorent, Nazim Haouchine, Fryderyk K\"ogl, Samuel Joutard, Parikshit Juvekar, Erickson Torio, Alexandra Golby, Sebastien Ourselin, Sarah Frisken, Tom Vercauteren, Tina Kapur, William M. Wells
• Abstract summary: We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation. Our model outperformed multi-modal VAEs, conditional GANs, and the current state-of-the-art unified method (ResViT) for missing images.
• Score: 34.821129614819604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images. Extensive experiments are performed on the challenging problem of joint intra-operative ultrasound (iUS) and Magnetic Resonance (MR) synthesis. Our model outperformed multi-modal VAEs, conditional GANs, and the current state-of-the-art unified method (ResViT) for synthesizing missing images, demonstrating the advantage of using a hierarchical latent representation and a principled probabilistic fusion operation. Our code is publicly available \url{https://github.com/ReubenDo/MHVAE}.

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