This book constitutes the refereed proceedings of the First International Workshop on Simulation and Synthesis in Medical Imaging, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 revised full papers presented together in this book were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories: fundamental methods for image-based biophysical modeling and image synthesis; biophysical and data-driven models of disease progression or organ development; biophysical and data-driven models of organ motion and deformation; biophysical and data-driven models of image formation and acquisition; segmentation/registration across or within modalities to aid the learning of model parameters; cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis; simulation and synthesis from large-scale image databases; automated techniques for quality assessment of simulations and synthetic images; as well as several applications of image synthesis and simulation in medical imaging such as image registration and segmentation; image denoising and information fusion; image reconstruction from sparse data or sparse views; and real-time simulation of biophysical properties. The papers were divided into two general topics named “simulation based approaches for medical imaging” and “synthesis and its applications in computational medical imaging”.

This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation. *The workshop was held virtually.

This book constitutes the refereed proceedings of the 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, image super resolution, and several applications of image synthesis and simulation for data augmentation, segmentation or lesion detection.

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.

This book constitutes the refereed proceedings of the Third International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected from numerous submissions. This workshop continues to provide a state-of-the-art and integrative perspective on simulation and synthesis in medical imaging for the purpose of invigorating research and stimulating new ideas on how to build theoretical links, practical synergies, and best practices between these two research directions.

This book constitutes the refereed proceedings of the 7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with MICCAI 2022, in Singapore, Singapore in September 2022.

The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.

This book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic. A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice. As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.