Daniel Rueckert – författare

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.

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.

CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice.

For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority.

LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice.

And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.

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 thoroughly refereedpost-workshop proceedings of the First International Workshop on Patch-based Techniquesin Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI2015, in Munich, Germany, in October 2015.

The 25 full papers presented in this volume werecarefully reviewed and selected from 35 submissions. The topics covered are suchas image segmentation of anatomical structures or lesions; image enhancement;computer-aided prognostic and diagnostic; multi-modality fusion; mono and multimodal image synthesis; image retrieval; dynamic, functional physiologic andanatomic imaging; super-pixel/voxel in medical image analysis; sparsedictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and4D+t data.

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.