Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.

This book constitutes the refereed proceedings of the 7th International Conference on Functional Imaging and Modeling of the Heart, held in London, UK, in June 2013. The 58 revised full papers were carefully reviewed and selected from numerous initial submissions. The focus of the papers is on following topics: image driven modeling, biophysical modeling, image analysis, biophysical modeling, cardiac imaging, parameter estimation, modeling methods, and biomedical engineering.

This book constitutes the refereed proceedings of the 5th International Workshop on Biomedical Image Registration, WBIR 2012, held in Nashville, Tennessee, USA, in July 2012. The 20 full papers and 11 poster papers included in this volume were carefully reviewed and selected from 44 submitted papers. They full papers are organized in the following topical sections: multiple image sets; brain; non-rigid anatomy; and frameworks and similarity measures.

The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009.

Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modeling and image formation; image registration; modeling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function.

The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009.

Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modeling and image formation; image registration; modeling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function.

This book constitutes the refereed proceedings of the 10th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Munich, Germany, in July 2022.The 11 full and poster papers together with 17 short papers included in this volume were carefully reviewed and selected from 32 submitted papers. The papers are organized in the following topical sections: optimization, deep learning architectures, neuroimaging, diffeomorphisms, uncertainty, topology and metrics.

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

This book constitutes the refereed proceedings of the Second International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2016, which was held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions. The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis.

This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015. The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data.