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Shape in Medical Imaging
This book constitutes the proceedings of the Workshop on Shape in Medical Imaging, ShapeMI 2018, held in conjunction with the 21st International Conference on Medical Image Computing, MICCAI 2018, in Granada, Spain, in September 2018. The 26 full papers and 2 short papers presented were carefully reviewed and selected for inclusion in this volume. The papers discuss novel approaches and applications in shape and geometry processing and their use in research and clinical studies and explore novel, cutting-edge theoretical methods and their usefulness for medical applications, e.g., from the fields of geometric learning or spectral shape analysis.
Shape in Medical Imaging
This volume comprises the proceedings of the International Workshop, ShapeMI 2023, which took place alongside MICCAI 2023 on October 8, 2023, in Vancouver, British Columbia, Canada. The 23 selected full papers deal with all aspects of leading methods and applications for advanced shape analysis and geometric learning in medical imaging.
Applications of Medical Artificial Intelligence
This book constitutes the refereed proceedings of the first International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions. Practical applications of medical AI bring in new challenges and opportunities. The AMAI workshop aims to engage medical AI practitioners and bring more application flavor in clinical, evaluation, human-AI collaboration, new technical strategy, trustfulness, etc., to augment the research and development on the application aspects of medical AI, on top of pure technical research.
Information Processing in Medical Imaging
This book constitutes the proceedings of the 28th International Conference on Information Processing in Medical Imaging, IPMI 2023, which took place in San Carlos de Bariloche, Argentina, in June 2023. The 63 full papers presented in this volume were carefully reviewed and selected from 169 submissions. They were organized in topical sections as follows: biomarkers; brain connectomics; computer-aided diagnosis/surgery; domain adaptation; geometric deep learning; groupwise atlasing; harmonization; federated learning; image synthesis; image enhancement; multimodal learning; registration; segmentation; self supervised learning; surface analysis and segmentation.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011
The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The second volume includes 83 papers organized in topical sections on diffusion weighted imaging, fMRI, statistical analysis and shape modeling, and registration.
Riemannian Geometric Statistics in Medical Image Analysis
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as sign...
Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures
This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Orthognathic Surgery
ORTHOGNATHIC SURGERY Orthognathic Surgery: Principles, Planning and Practice is a definitive clinical guide to orthognathic surgery, from initial diagnosis and treatment planning to surgical management and postoperative care. Addresses the major craniofacial anomalies and complex conditions of the jaw and face that require surgery Edited by two highly experienced specialists, with contributions from an international team of experts Enhanced by case studies, note boxes and more than 2000 clinical photographs and illustrations Serves as an essential reference for higher trainees and practicing clinicians in cranio-maxillofacial surgery, orthodontics, plastic and reconstructive surgery and allied specialties
Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers
This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.
