Magnetic Resonance Imaging, Computed Tomography, and other image modalities are routinely used to visualize a particular structure in the patient's body. The classification of the image region corresponding to this structure is called segmentation. For applications in Neuroscience, it is important for the segmentation of a brain scan to represent the boundary of the brain as a single folded sheet. However the segmentation of the brain generally exhibits many erroneous holes. Consequently we have developed an algorithm for automatically removing holes in segmented medical scans while preserving the accuracy of the image. Upon concepts of Discrete Topology, we correct the holes based on the smallest modification to the image. First we detect each hole with a front propagation and a Reeb graph. Then we search for a number of loops around the hole on the isosurface of the image. Finally we select the loop that minimizes the modification to the image and correct the hole in the image. At each step we limit the size of the data in memory. With these contributions our algorithm removes every hole in the image with high accuracy and low complexity even for images that do not fit into the main memory. To help doctors and scientists to obtain segmentations without holes, we make our software publicly available.

This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses. It is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors.

Among the components of the limbic system, the amygdala is a fascinating structure that is involved in the processes of liking and disliking and in the ways our emotions drive our actions and affect the strength of our memories. Combined with new conceptual breakthroughs, the very latest data obtained by leading world experts in amygdala function that are reviewed in this book have helped to understand how the amygdala contribute to these processes and also to a variety of neurological and neuropsychiatric pathologies. Of course, due to the rate of research advancement, all the chapters presented here correspond to precise questions addressed by experts using highly specific techniques. Therefore, each chapter should be viewed as pieces of a puzzle that represent all the different research areas that have to be taken into consideration in discussing the role of the amygdala in emotion and memory. Although the primary goal of this book is to inform experts and newcomers of some of the latest data in the field of brain structures involved in mechanisms underlying emotional learning and memory, we hope it will also help to stimulate discussion on the functional role of the amygdala and connected brain structures in these mechanisms.

Annotation The two-volume set LNCS 5241 and LNCS 5242 constitute the refereed proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, held in New York, NY, USA, in September 2008.The program committee carefully selected 258 revised papers from numerous submissions for presentation in two volumes, based on rigorous peer reviews. The first volume includes 127 papers related to medical image computing, segmentation, shape and statistics analysis, modeling, motion tracking and compensation, as well as registration. The second volume contains 131 contributions related to robotics and interventions, statistical analysis, segmentation, intervention, modeling, and registration.

This book constitutes the refereed proceedings of the 20th International Conference on Information Processing in Medical Imaging, IPMI 2007, held in Kerkrade, The Netherlands, in July 2007. It covers segmentation, cardiovascular imaging, detection and labeling, diffusion tensor imaging, registration, image reconstruction, functional brain imaging, as well as shape models and registration.

The nineteenth biennial International Conference on Information Processing in Medical Imaging (IPMI) was held July 11–15, 2005 in Glenwood Springs, CO, USA on the Spring Valley campus of the Colorado Mountain College. Following the successful meeting in beautiful Ambleside in England, this year’s conference addressed important recent developments in a broad range of topics related to the acquisition, analysis and application of biomedical images. Interest in IPMI has been steadily growing over the last decade. This is p- tially due to the increased number of researchers entering the ?eld of medical imagingasaresultoftheWhitakerFoundationandtherecentlyformedNational Institute of Biomedical Imaging and Bioengineering. This year, there were 245 full manuscripts submitted to the conference which was twice the number s- mitted in 2003 and almost four times the number of submissions in 2001. Of these papers, 27 were accepted as oral presentations, and 36 excellent subm- sions that could not be accommodated as oral presentations were presented as posters. Selection of the papers for presentation was a di?cult task as we were unable to accommodate many of the excellent papers submitted this year. All accepted manuscripts were allocated 12 pages in these proceedings.

This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

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.

This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, held in conjunction with MICCAI 2010, in Beijing, China, on September 20, 2010. The 19 revised full papers presented were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on CT colonography CAD, abdominal imaging, and virtual colonoscopy.

The book covers novel strategies of state of the art in engineering and clinical analysis and approaches for analyzing abdominal imaging, including lung, mediastinum, pleura, liver, kidney and gallbladder. In the last years the imaging techniques have experienced a tremendous improvement in the diagnosis and characterization of the pathologies that affect abdominal organs. In particular, the introduction of extremely fast CT scanners and high Magnetic field MR Systems allow imaging with an exquisite level of detail the anatomy and pathology of liver, kidney, pancreas, gallbladder as well as lung and mediastinum. Moreover, thanks to the development of powerful computer hardware and advanced mathematical algorithms the quantitative and automated\semi automated diagnosis of the pathology is becoming a reality. Medical image analysis plays an essential role in the medical imaging field, including computer-aided diagnosis, organ/lesion segmentation, image registration, and image-guided therapy. This book will cover all the imaging techniques, potential for applying such imaging clinically, and offer present and future applications as applied to the abdomen and thoracic imaging with the most world renowned scientists in these fields. The main aim of this book is to help advance scientific research within the broad field of abdominal imaging. This book focuses on major trends and challenges in this area, and it presents work aimed to identify new techniques and their use in medical imaging analysis for abdominal imaging. ​