Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications

Le coeur numérique personnalisé

International audienceDuring the last past years, significant progress in Medical Image Analysis, in biomathematics and biophysics have led to development of the first personalized digital cardiac models. These digital models are personalized which means they can reproduce the anatomy and physiology of specific patients. They allow the quantitative analysis of the organ function and the simulation of some therapies to evaluate their expected benefit. In this article we describe some recent research work done on these topics in our project team Asclepios at Inria, in collaboration with other Inria teams (Macs, Reo, Sisyphe) and external academic, clinical and industrial partners. If a number of challenges in mathematics and informatics still have to be solved before such personalized digital cardiac models can be used in current clinical practice, these first results announce a new generation of tools in digital medicine which can contribute more widely to a more preventive and more predictive personalized medicine.Au cours de ces dernières années, des progrès importants dans l'analyse informatique des images médicales et dans la modélisation biomathématique et biophysique ont rendu possible la construction des premiers modèles numériques et personnalisés du cœur humain. Ces modèles informatiques sont personnalisés car ils reproduisent l'anatomie et la physiologie de patients spécifiques. Ils permettent d'analyser et de quantifier le fonctionnement de l'organe et de simuler certainesthérapies pour en évaluer le bénéfice espéré. Dans cet article nous décrivons des travaux de recherche récents réalisés sur ce thème au sein de l'équipe projet Asclepios à l'Inria, en collaboration avec d'autres équipes Inria (Macs, Reo, Sisyphe) et des partenaires extérieurs académiques, cliniques et industriels. Si de grands défis en modélisation informatique et mathématique doivent encore être relevés avant une utilisation clinique courante du cœur numérique personnalisé, ces premiers résultats annoncent une nouvelle génération d'outils de médecine numérique pouvant contribuer plus largement à une médecine personnalisée plus préventive et plus prédictive