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

Automatic 3D segmentation of the prostate on magnetic resonance images for radiotherapy planning

Abstract. Accurate segmentation of the prostate, the seminal vesicles, the bladder and the rectum is a crucial step for planning radiotherapy (RT) procedures. Modern radiotherapy protocols have included the delineation of the pelvic organs in magnetic resonance images (MRI), as the guide to the therapeutic beam irradiation over the target organ. However, this task is highly inter and intra-expert variable and may take about 20 minutes per patient, even for trained experts, constituting an important burden in most radiological services. Automatic or semi-automatic segmentation strategies might then improve the efficiency by decreasing the measured times while conserving the required accuracy. This thesis presents a fully automatic prostate segmentation framework that selects the most similar prostates w.r.t. a test prostate image and combines them to estimate the segmentation for the test prostate. A robust multi-scale analysis establishes the set of most similar prostates from a database, independently of the acquisition protocol. Those prostates are then non-rigidly registered towards the test image and fusioned by a linear combination. The proposed approach was evaluated using a MRI public dataset of patients with benign hyperplasia or cancer, following different acquisition protocols, namely 26 endorectal and 24 external. Evaluating under a leave-one-out scheme, results show reliable segmentations, obtaining an average dice coefficient of 79%, when comparing with the expert manual segmentation.La delineación exacta de la próstata, las vesículas seminales, la vejiga y el recto es un paso fundamental para el planeamiento de procedimientos de radioterapia. Protocolos modernos han incluido la delineación de los órganos pélvicos en imágenes de resonancia magnética (IRM), como la guia para la irradiación del haz terapéutico sobre el órgano objetivo. Sin embargo, esta tarea es altamente variable intra e inter-experto y puede tomar al rededor de 20 minutos por paciente, incluso para expertos entrenados, convirtiéndose en una carga importante en la mayoría de los servicios de radiología. Métodos automáticos o semi-automáticos podrían mejorar la eficiencia disminuyendo los tiempos medidos mientras se conserva la precisión requerida. Este trabajo presenta una estrategia de segmentación de la próstata completamente automático que selecciona las prostatas más similares con respecto a una imagen de resonancia magnética de prueba y combina las delineaciones asociadas a dichas imágenes para estimar la segmentación de la imagen de prueba. Un análisis multiescala robusto permite establecer el conjunto de las próstatas más parecidas de una base de datos, independiente del protocolo de adquisición. Las imágenes seleccionadas son registradas de forma no rigida con respecto a la imagen de prueba y luego son fusionadas mediante una combinación lineal. El enfoque propuesto fue evaluado utilizando un conjunto público de imágenes de resonancia magnética de pacientes con hiperplasia benigna o con cancer, con diferentes protocolos de adquisición, esto es 26 externas y 24 endorectales. Este trabajo fue evaluado bajo un esquema leave-one-out, cuyos resultados mostraron segmentaciones confiables, obteniendo un DSC promedio de 79%, cuando se compararon los resultados obtenidos con las segmentaciones manuales de expertos.Maestrí

Treatment of Later Humoral Rejection with Anti-CD20 Monoclonal Antibody Rituximab: A Single Centre Experience

Humoral or vascular rejection is a B cell-mediated production of immunoglobulin (Ig) G antibody against a transplanted organ that results in immune complex deposition on the vascular endothelium, activation of the complement cascade, production of endothelial dysfunction and regional ischaemic injury