Clasificación de imágenes dermatoscópicas

Este proyecto presenta un software para el análisis de imágenes dermatoscópicas correspondiente a lesiones melanocíticas, con el fin de clasificarlas entre lesiones benignas y melanoma. El sistema realiza una segmentación automática de la lesión y la procesa en varas etapas, extrayendo características de relevancia diagnóstica: asimetría, colores, irregularidad del borde, y la presencia de estructuras como redes pigmentadas atípicas o velo azul-blanquecino. Proporciona además una herramienta para el etiquetado manual de estructuras adicionales. La clasificación automática de las lesiones se realiza en base a los métodos de diagnóstico más comúnmente utilizados: las reglas ABCD, Menzies, 7-point checklist, CASH y CHAOS & CLUES. El sistema de clasificación se evalúa sobre una base de datos de imágenes dermatoscópicas, y se realiza una comparativa de los resultados obtenidos por cada método de diagnóstico. ABSTRACT. This project presents a software for the analysis of dermoscopic images of melanocytic lesions, and their classification into benign lesions and melanoma. The system performs automatic segmentation of the lesion and goes through several stages of extraction of certain characteristics relevant to the diagnosis, such as asymmetry, border irregularity, or presence of structures like atypical pigmented network or blue-whitish veil. Automatic classification of the lesions is accomplished by means of the most commonly used diagnostic methods, such as ABCD and Menzies's rules, the 7-point checklist, CASH, and CHAOS & CLUES. The classification system is evaluated by using a dermoscopic image database, and a comparison of the results yielded by the different diagnostic methods is performed

Novel miniaturised and highly versatile biomechatronic platforms for the characterisation of melanoma cancer cells

There has been an increasing demand to acquire highly sensitive devices that are able to detect and characterize cancer at a single cell level. Despite the moderate progress in this field, the majority of approaches failed to reach cell characterization with optimal sensitivity and specificity. Accordingly, in this study highly sensitive, miniaturized-biomechatronic platforms have been modeled, designed, optimized, microfabricated, and characterized, which can be used to detect and differentiate various stages of melanoma cancer cells. The melanoma cell has been chosen as a legitimate cancer model, where electrophysiological and analytical expression of cell-membrane potential have been derived, and cellular contractile force has been obtained through a correlation with micromechanical deflections of a miniaturized cantilever beam. The main objectives of this study are in fourfold: (1) to quantify cell-membrane potential, (2) correlate cellular biophysics to respective contractile force of a cell in association with various stages of the melanoma disease, (3) examine the morphology of each stage of melanoma, and (4) arrive at a relation that would interrelate stage of the disease, cellular contractile force, and cellular electrophysiology based on conducted in vitro experimental findings. Various well-characterized melanoma cancer cell lines, with varying degrees of genetic complexities have been utilized. In this study, two-miniaturized-versatile-biomechatronic platforms have been developed to extract the electrophysiology of cells, and cellular mechanics (mechanobiology). The former platform consists of a microfluidic module, and stimulating and recording array of electrodes patterned on a glass substrate, forming multi-electrode arrays (MEAs), whereas the latter system consists of a microcantilever-based biosensor with an embedded Wheatstone bridge, and a microfluidic module. Furthermore, in support of this work main objectives, dedicated microelectronics together with customized software have been attained to functionalize, and empower the two-biomechatronic platforms. The bio-mechatronic system performance has been tested throughout a sufficient number of in vitro experiments.Open Acces