RESUMEN
Automated detection of amyloid plaques (AP) in post mortem brain sections of patients with Alzheimer disease (AD) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. We propose a new segmentation method to automatically detect amyloid plaques in Congo Red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. A set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation.
Asunto(s)
Enfermedad de Alzheimer/patología , Encéfalo/patología , Colorimetría/métodos , Rojo Congo , Modelos Animales de Enfermedad , Interpretación de Imagen Asistida por Computador/métodos , Placa Amiloide/patología , Algoritmos , Animales , Inteligencia Artificial , Medios de Contraste , Humanos , Aumento de la Imagen/métodos , Ratones , Ratones Transgénicos , Reconocimiento de Normas Patrones Automatizadas/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Automated detection of amyloid plaques (AP) in post mortem brain sections of patients with Alzheimer disease (AD) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. We propose a new segmentation method to automatically detect amyloid plaques in Congo Red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. A set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation. Original information concerning global amyloid load have been derived from 6 mouse brains which opens new perspectives for the extensive analysis of such a data in 3-D and the possibility to integrate in vivo-post mortem information for diagnosis purposes.