Titlebook: Simulation and Synthesis in Medical Imaging; 8th International Wo Jelmer M. Wolterink,David Svoboda,Virginia Fernand Conference proceedings

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Conference proceedings 2023rom 16 submissions. They span a wide range of topics relevant to SASHIMI, and reflect recent developments in methods for segmentation, image-to-image?translation, super-resolution, and image synthesis. Applications include MRI?imaging, echocardiography, PET, and digital pathology..

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978-3-031-44688-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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,Transformers for?CT Reconstruction from?Monoplanar and?Biplanar Radiographs,ages are first embedded into latent quantized codebook vectors using two different autoencoder networks. We then train a GPT model, to reconstruct the codebook vectors of the CT image, conditioned on the codebook vectors of the x-rays and show that this approach leads to realistic looking images. To

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,Learned Local Attention Maps for?Synthesising Vessel Segmentations from?T2 MRI,tic vessel segmentations generated from only T2 MRI achieved a mean Dice score of . in testing, compared to state-of-the-art segmentation networks such as transformer U-Net (.) and nnU-net(.), while using only a fraction of the parameters. The main qualitative difference between our synthetic vessel

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,Unsupervised Heteromodal Physics-Informed Representation of?MRI Data: Tackling Data Harmonisation, paired qualitative-quantitative data. Furthermore, we make the proposed model robust to missing data, enabling us to map any arbitrary set of qualitative data from a patient into quantitative Multi-Parametric Maps (MPMs). We demonstrate that the estimated MPMs are a robust and invariant data represe