Pulmonary Artery-Vein Segmentation in Real and Synthetic CT Images = Segmentación Arteria-Vena Pulmonares en Imágenes de CT Reales y Sintéticas

La tomografía axial computerizada (TAC) es la modalidad de imagen médica preferente para el estudio de enfermedades pulmonares y el análisis de su vasculatura. La segmentación general de vasos en pulmón ha sido abordada en profundidad a lo largo de los últimos años por la comunidad científica que trabaja en el campo de procesamiento de imagen; sin embargo, la diferenciación entre irrigaciones arterial y venosa es aún un problema abierto. De hecho, la separación automática de arterias y venas está considerado como uno de los grandes retos futuros del procesamiento de imágenes biomédicas. La segmentación arteria-vena (AV) permitiría el estudio de ambas irrigaciones por separado, lo cual tendría importantes consecuencias en diferentes escenarios médicos y múltiples enfermedades pulmonares o estados patológicos. Características como la densidad, geometría, topología y tamaño de los vasos sanguíneos podrían ser analizados en enfermedades que conllevan remodelación de la vasculatura pulmonar, haciendo incluso posible el descubrimiento de nuevos biomarcadores específicos que aún hoy en dípermanecen ocultos. Esta diferenciación entre arterias y venas también podría ayudar a la mejora y el desarrollo de métodos de procesamiento de las distintas estructuras pulmonares. Sin embargo, el estudio del efecto de las enfermedades en los árboles arterial y venoso ha sido inviable hasta ahora a pesar de su indudable utilidad. La extrema complejidad de los árboles vasculares del pulmón hace inabordable una separación manual de ambas estructuras en un tiempo realista, fomentando aún más la necesidad de diseñar herramientas automáticas o semiautomáticas para tal objetivo. Pero la ausencia de casos correctamente segmentados y etiquetados conlleva múltiples limitaciones en el desarrollo de sistemas de separación AV, en los cuales son necesarias imágenes de referencia tanto para entrenar como para validar los algoritmos. Por ello, el diseño de imágenes sintéticas de TAC pulmonar podría superar estas dificultades ofreciendo la posibilidad de acceso a una base de datos de casos pseudoreales bajo un entorno restringido y controlado donde cada parte de la imagen (incluyendo arterias y venas) está unívocamente diferenciada. En esta Tesis Doctoral abordamos ambos problemas, los cuales están fuertemente interrelacionados. Primero se describe el diseño de una estrategia para generar, automáticamente, fantomas computacionales de TAC de pulmón en humanos. Partiendo de conocimientos a priori, tanto biológicos como de características de imagen de CT, acerca de la topología y relación entre las distintas estructuras pulmonares, el sistema desarrollado es capaz de generar vías aéreas, arterias y venas pulmonares sintéticas usando métodos de crecimiento iterativo, que posteriormente se unen para formar un pulmón simulado con características realistas. Estos casos sintéticos, junto a imágenes reales de TAC sin contraste, han sido usados en el desarrollo de un método completamente automático de segmentación/separación AV. La estrategia comprende una primera extracción genérica de vasos pulmonares usando partículas espacio-escala, y una posterior clasificación AV de tales partículas mediante el uso de Graph-Cuts (GC) basados en la similitud con arteria o vena (obtenida con algoritmos de aprendizaje automático) y la inclusión de información de conectividad entre partículas. La validación de los fantomas pulmonares se ha llevado a cabo mediante inspección visual y medidas cuantitativas relacionadas con las distribuciones de intensidad, dispersión de estructuras y relación entre arterias y vías aéreas, los cuales muestran una buena correspondencia entre los pulmones reales y los generados sintéticamente. La evaluación del algoritmo de segmentación AV está basada en distintas estrategias de comprobación de la exactitud en la clasificación de vasos, las cuales revelan una adecuada diferenciación entre arterias y venas tanto en los casos reales como en los sintéticos, abriendo así un amplio abanico de posibilidades en el estudio clínico de enfermedades cardiopulmonares y en el desarrollo de metodologías y nuevos algoritmos para el análisis de imágenes pulmonares. ABSTRACT Computed tomography (CT) is the reference image modality for the study of lung diseases and pulmonary vasculature. Lung vessel segmentation has been widely explored by the biomedical image processing community, however, differentiation of arterial from venous irrigations is still an open problem. Indeed, automatic separation of arterial and venous trees has been considered during last years as one of the main future challenges in the field. Artery-Vein (AV) segmentation would be useful in different medical scenarios and multiple pulmonary diseases or pathological states, allowing the study of arterial and venous irrigations separately. Features such as density, geometry, topology and size of vessels could be analyzed in diseases that imply vasculature remodeling, making even possible the discovery of new specific biomarkers that remain hidden nowadays. Differentiation between arteries and veins could also enhance or improve methods processing pulmonary structures. Nevertheless, AV segmentation has been unfeasible until now in clinical routine despite its objective usefulness. The huge complexity of pulmonary vascular trees makes a manual segmentation of both structures unfeasible in realistic time, encouraging the design of automatic or semiautomatic tools to perform the task. However, this lack of proper labeled cases seriously limits in the development of AV segmentation systems, where reference standards are necessary in both algorithm training and validation stages. For that reason, the design of synthetic CT images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image (including arteries and veins) is differentiated unequivocally. In this Ph.D. Thesis we address both interrelated problems. First, the design of a complete framework to automatically generate computational CT phantoms of the human lung is described. Starting from biological and imagebased knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. These synthetic cases, together with labeled real CT datasets, have been used as reference for the development of a fully automatic pulmonary AV segmentation/separation method. The approach comprises a vessel extraction stage using scale-space particles and their posterior artery-vein classification using Graph-Cuts (GC) based on arterial/venous similarity scores obtained with a Machine Learning (ML) pre-classification step and particle connectivity information. Validation of pulmonary phantoms from visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems, show good correspondence between real and synthetic lungs. The evaluation of the Artery-Vein (AV) segmentation algorithm, based on different strategies to assess the accuracy of vessel particles classification, reveal accurate differentiation between arteries and vein in both real and synthetic cases that open a huge range of possibilities in the clinical study of cardiopulmonary diseases and the development of methodological approaches for the analysis of pulmonary images.

Venous thrombosis and pulmonary embolism /

Mode of access: Internet.

Postmortem Demonstration of the Source of Pulmonary Thromboembolism: The Importance of the Autopsy

Periprostatic or paravaginal venous thromboses are rarely considered clinically as sites of clot origin in patients with pulmonary thromboembolism. The majority of emboli have been demonstrated to originate in the veins of the legs. This report raises awareness of pelvic vein thrombosis as a potential source of pulmonary embolism that is rarely considered or detected clinically, and which usually requires postmortem examination for recognition. It also reviews the possible routes emboli may take to reach the lungs.

Nurse-determined assessment of cardiac output. Comparing a non-invasive cardiac output device and pulmonary artery catheter: a prospective observational study.

Background The accurate measurement of Cardiac output (CO) is vital in guiding the treatment of critically ill patients. Invasive or minimally invasive measurement of CO is not without inherent risks to the patient. Skilled Intensive Care Unit (ICU) nursing staff are in an ideal position to assess changes in CO following therapeutic measures. The USCOM (Ultrasonic Cardiac Output Monitor) device is a non-invasive CO monitor whose clinical utility and ease of use requires testing. Objectives To compare cardiac output measurement using a non-invasive ultrasonic device (USCOM) operated by a non-echocardiograhically trained ICU Registered Nurse (RN), with the conventional pulmonary artery catheter (PAC) using both thermodilution and Fick methods. Design Prospective observational study. Setting and participants Between April 2006 and March 2007, we evaluated 30 spontaneously breathing patients requiring PAC for assessment of heart failure and/or pulmonary hypertension at a tertiary level cardiothoracic hospital. Methods SCOM CO was compared with thermodilution measurements via PAC and CO estimated using a modified Fick equation. This catheter was inserted by a medical officer, and all USCOM measurements by a senior ICU nurse. Mean values, bias and precision, and mean percentage difference between measures were determined to compare methods. The Intra-Class Correlation statistic was also used to assess agreement. The USCOM time to measure was recorded to assess the learning curve for USCOM use performed by an ICU RN and a line of best fit demonstrated to describe the operator learning curve. Results In 24 of 30 (80%) patients studied, CO measures were obtained. In 6 of 30 (20%) patients, an adequate USCOM signal was not achieved. The mean difference (±standard deviation) between USCOM and PAC, USCOM and Fick, and Fick and PAC CO were small, −0.34 ± 0.52 L/min, −0.33 ± 0.90 L/min and −0.25 ± 0.63 L/min respectively across a range of outputs from 2.6 L/min to 7.2 L/min. The percent limits of agreement (LOA) for all measures were −34.6% to 17.8% for USCOM and PAC, −49.8% to 34.1% for USCOM and Fick and −36.4% to 23.7% for PAC and Fick. Signal acquisition time reduced on average by 0.6 min per measure to less than 10 min at the end of the study. Conclusions In 80% of our cohort, USCOM, PAC and Fick measures of CO all showed clinically acceptable agreement and the learning curve for operation of the non-invasive USCOM device by an ICU RN was found to be satisfactorily short. Further work is required in patients receiving positive pressure ventilation.

When and how to treat pulmonary non-tuberculous mycobacterial diseases

Nontuberculous mycobacteria are ubiquitous environmental organisms that have been recognised as a cause of pulmonary infection for over 50 years. Traditionally patients have had underlying risk factors for development of disease; however the proportion of apparently immunocompetent patients involved appears to be rising. Not all patients culture-positive for mycobacteria will have progressive disease, making the diagnosis difficult, though criteria to aid in this process are available. The two main forms of disease are cavitary disease (usually involving the upper lobes) and fibronodular bronchiectasis (predominantly middle and lingular lobes). For patients with disease, combination antibiotic therapy for 12-24 months is generally required for successful treatment, and this may be accompanied by drug intolerances and side effects. Published success rates range from 30-82%. As the progression of disease is variable, for some patients, attention to pulmonary hygiene and underlying diseases without immediate antimycobacterial therapy may be more appropriate. Surgery can be a useful adjunct, though is associated with risks. Randomised controlled trials in well described patients would provide stronger evidence-based data to guide therapy of NTM lung diseases, and thus are much needed.

High-density lipoprotein and the risk of recurrent venous thromboembolism

BACKGROUND - High-density lipoprotein (HDL) protects against arterial atherothrombosis, but it is unknown whether it protects against recurrent venous thromboembolism. METHODS AND RESULTS - We studied 772 patients after a first spontaneous venous thromboembolism (average follow-up 48 months) and recorded the end point of symptomatic recurrent venous thromboembolism, which developed in 100 of the 772 patients. The relationship between plasma lipoprotein parameters and recurrence was evaluated. Plasma apolipoproteins AI and B were measured by immunoassays for all subjects. Compared with those without recurrence, patients with recurrence had lower mean (±SD) levels of apolipoprotein AI (1.12±0.22 versus 1.23±0.27 mg/mL, P<0.001) but similar apolipoprotein B levels. The relative risk of recurrence was 0.87 (95% CI, 0.80 to 0.94) for each increase of 0.1 mg/mL in plasma apolipoprotein AI. Compared with patients with apolipoprotein AI levels in the lowest tertile (<1.07 mg/mL), the relative risk of recurrence was 0.46 (95% CI, 0.27 to 0.77) for the highest-tertile patients (apolipoprotein AI >1.30 mg/mL) and 0.78 (95% CI, 0.50 to 1.22) for midtertile patients (apolipoprotein AI of 1.07 to 1.30 mg/mL). Using nuclear magnetic resonance, we determined the levels of 10 major lipoprotein subclasses and HDL cholesterol for 71 patients with recurrence and 142 matched patients without recurrence. We found a strong trend for association between recurrence and low levels of HDL particles and HDL cholesterol. CONCLUSIONS - Patients with high levels of apolipoprotein AI and HDL have a decreased risk of recurrent venous thromboembolism. © 2007 American Heart Association, Inc.