Buch, Englisch, 142 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
8th International Workshop, SASHIMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Buch, Englisch, 142 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-44688-7
Verlag: Springer Nature Switzerland
This book constitutes the refereed proceedings of the 8th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023.
The 13 full papers included in this book were carefully reviewed and selected from 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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Research
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Weitere Infos & Material
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs Super-resolution Segmentation network for inner-ear tissue segmentation.- Multi-Phase Liver-Specific DCE-MRI Translation via a Registration-Guided GAN.- Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI Physics-Aware Motion Simulation for T2*-Weighted Brain MRI.- Unsupervised heteromodal physics-informed representation of MRI data: tackling data harmonisation, imputation and domain shift.- TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction.- How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis.- Improving style transfer in dynamic contrast enhanced MRI using a spatio-temporal approach Synthetic Singleplex-Image Generation in Multiplex-Brightfield Immunohistochemistry.- Digital Pathology using Deep Generative Models Self-Supervised Super-Resolution for Anisotropic MR Images with and without Slice Gap DIFF·3: A latent diffusion model for the generation of synthetic 3D echocardiographic images and corresponding labels.
