Anotación Automática de Imágenes Médicas Usando la Representación de Bolsa de Características

La anotación automática de imágenes médicas se ha convertido en un proceso necesario para la gestión, búsqueda y exploración de las crecientes bases de datos médicas para apoyo al diagnóstico y análisis de imágenes en investigación biomédica. La anotación automática consiste en asignar conceptos de alto nivel a imágenes a partir de las características visuales de bajo nivel. Para esto se busca tener una representación de la imagen que caracterice el contenido visual de ésta y un modelo de aprendizaje entrenado con ejemplos de imágenes anotadas. Este trabajo propone explorar la Bolsa de Características (BdC) para la representación de las imágenes de histología y los Métodos de Kernel (MK) como modelos de aprendizaje de máquina para la anotación automática. Adicionalmente se exploró una metodología de análisis de colecciones de imágenes para encontrar patrones visuales y sus relaciones con los conceptos semánticos usando Análisis de Información Mutua, Selección de Características con Máxima-Relevancia y Mínima-Redundancia (mRMR) y Análisis de Biclustering. La metodología propuesta fue evaluada en dos bases de datos de imágenes, una con imá- genes anotadas con los cuatro tejidos fundamentales y otra con imágenes de tipo de cáncer de piel conocido como carcinoma basocelular. Los resultados en análisis de imágenes revelan que es posible encontrar patrones implícitos en colecciones de imágenes a partir de la representación BdC seleccionan- do las palabras visuales relevantes de la colección y asociándolas a conceptos semánticos mientras que el análisis de biclustering permitió encontrar algunos grupos de imágenes similares que comparten palabras visuales asociadas al tipo de tinción o conceptos. En anotación automática se evaluaron distintas configuraciones del enfoque BdC. Los mejores resultados obtenidos presentan una Precisión de 91 % y un Recall de 88 % en las imágenes de histología, y una Precisión de 59 % y un Recall de 23 % en las imágenes de histopatología. La configuración de la metodología BdC con los mejores resultados en ambas colecciones fue obtenida usando las palabras visuales basadas en DCT con un diccionario de tamaño 1,000 con un kernel Gaussiano. / Abstract. The automatic annotation of medical images has become a necessary process for managing, searching and exploration of growing medical image databases for diagnostic support and image analysis in biomedical research. The automatic annotation is to assign high-level concepts to images from the low-level visual features. For this, is needed to have a image representation that characterizes its visual content and a learning model trained with examples of annotated images. This paper aims to explore the Bag of Features (BOF) for the representation of histology images and Kernel Methods (KM) as models of machine learning for automatic annotation. Additionally, we explored a methodology for image collection analysis in order to _nd visual patterns and their relationships with semantic concepts using Mutual Information Analysis, Features Selection with Max-Relevance and Min- Redundancy (mRMR) and Biclustering Analysis. The proposed methodology was evaluated in two image databases, the _rst have images annotated with the four fundamental tissues, and the second have images of a type of skin cancer known as Basal-cell carcinoma. The image analysis results show that it is possible to _nd implicit patterns in image collections from the BOF representation. This by selecting the relevant visual words in the collection and associating them with semantic concepts, whereas biclustering analysis allowed to _nd groups of similar images that share visual words associated with the type of stain or concepts. The Automatic annotation was evaluated in di_erent settings of BOF approach. The best results have a Precision of 91% and Recall of 88% in the histology images, and a Precision of 59% and Recall of 23% in histopathology images. The con_guration of BOF methodology with the best results in both datasets was obtained using the DCT-based visual words in a dictionary size of 1; 000 with a Gaussian kernel.Maestrí

Development of a 3D Mouse Atlas Tool for Improved Non-Invasive Imaging of Orthotopic Mouse Models of Pancreatic Cancer.

PhD ThesesPancreatic cancer is the 10th most common cancer in the UK with 10,000 people a year being diagnosed. This form of cancer also has one of the lowest survival rates, with only 5% of patient surviving for 5 years (1). There has not been significant progress in the treatment of pancreatic cancer for the last 30 years (1). Recognition of this historic lack of progress has led to an increase in research effort and funding aimed at developing novel treatments for pancreatic cancer. This in turn has had an inflationary effect on the numbers of animals being used to study the effects of these treatments. Genetically engineered mouse models (GEMMs) are currently thought to be most appropriate for these types of studies as the manner in which the mice develop pancreatic tumours is much closer to that seen in the clinic. One such GEMM is the K-rasLSL.G12D/+;p53R172H/+;PdxCre (KPC) model (2) in which the mouse is born with normal pancreas and then develops PanIN lesions (one of the main lesions linked to pancreatic ductal adenocarcinoma (PDAC) (2)) at an accelerated rate. The KPC model is immune competent and because the tumours develop orthotopically in the pancreas, they have a relevant microenvironment and stromal makeup, suitable for testing of new therapeutic approaches. Unlike the human pancreas which is regular in shape, the mouse pancreas is a soft and spongy organ that has its dimensions defined to a large extent by the position of the organs that surround it, such as the kidney, stomach and spleen (3). This changes as pancreatic tumours develop, with the elasticity of the pancreas decreasing as the tissue becomes more desmoplastic. Because the tumours are deep within the body, disease burden is difficult to assess except by sacrificing groups of animals or by using non-invasive imaging. Collecting data by sacrificing groups of animals at different timepoints results in use of very high numbers per study. This is in addition to the fact that in the KPC model (similar to other GEMMs), fewer than 25% have the desired genetic makeup, meaning that 3-4 animals are destroyed for every one that is put into study (2). Therefore, in order to reduce the numbers of animals used in 5 pancreatic research, a non-invasive imaging tool that allows accurate assessment of pancreatic tumour burden longitudinally over time has been developed. Magnetic resonance imaging (MRI) has been used as it is not operator dependent (allowing it to be used by non-experts) and does not use ionising radiation which is a potential confounding factor when monitoring tumour development. The tool has been developed for use with a low field instrument (1T) which ensures its universal applicability as it will perform even better when used with magnets of field strength higher than 1T. This work has been carried out starting from an existing 3D computational mouse atlas and developing a mathematical model that can automatically detect and segment mouse pancreas as well as pancreatic tumours in MRI images. This has been achieved using multiple image analysis techniques including thresholding, texture analysis, object detection, edge detection, multi-atlas segmentation, and machine learning. Through these techniques, unnecessary information is removed from the image, the area of analysis is reduced, the pancreas is isolated (and then classified healthy or unhealthy), and - if unhealthy - the pancreas is evaluated to identify tumour location and volume. This semi-automated approach aims to aid researchers by reducing image analysis time (especially for non-expert users) and increasing both objectivity and statistical accuracy. It facilitates the use of MRI as a method of longitudinally tracking tumour development and measuring response to therapy in the same animal, thus reducing biological variability and leading to a reduction in group size. The MR images of mice and pancreatic tumours used in this work were obtained through studies already being conducted in order to reduce the number of animals used without having to compromise on the validity of results