Deep learning methods for extraction of neuroimage markers in the prognosis of brain pathologies
This PhD thesis focuses on improving the extraction of neuroimage markers for the prognosis and outcome prediction of neurological pathologies such as ischemic stroke, Alzheimer’s disease (AD) and multiple sclerosis (MS). Our work has been developed on two of the most relevant neuroimage markers for...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/688369 |
| Acceso en línea: | http://hdl.handle.net/10803/688369 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge profund Aprendizaje profundo Deep learning Lesions cerebrals Lesiones cerebrales Brain lesions Atròfia cerebral Atrofia cerebral Cerebral atrophy Imatges per ressonància magnètica Imágenes por resonancia magnética Magnetic resonance imaging Ressonància magnètica cerebral Resonancia magnética cerebral Brain MRI Xarxes neuronals convolucionals Redes neuronales convolucionales Convolutional neural networks Segmentació de teixits Segmentación de tejidos Tissue segmentation 004 616.8 |
| Sumario: | This PhD thesis focuses on improving the extraction of neuroimage markers for the prognosis and outcome prediction of neurological pathologies such as ischemic stroke, Alzheimer’s disease (AD) and multiple sclerosis (MS). Our work has been developed on two of the most relevant neuroimage markers for diagnosis and prediction, brain lesion segmentation and longitudinal atrophy quantification. Brain lesion segmentation can be directly used in MS and ischemic stroke as a prognostic marker and can also be useful for other downstream segmentation tasks. In MS, disease activity produces very characteristic lesions which can help with diagnosis and prognosis of the pathology. In ischemic stroke, lesion segmentation can inform the treatment decision workflow by quantifying the amount of tissue that could be salvaged against the risks of surgical intervention. We also tackle in this PhD thesis the task of brain tissue segmentation for longitudinal atrophy quantification, a validated prognostic image marker in MS and AD. Measurements of longitudinal atrophy can be used to assess the rate of disease progression and might even help to predict AD onset years in advance. In MS patients, an accelerated rate of brain atrophy is also observed as a result of disease activity and is used as a prognostic marker and to evaluate the response of disease-modifying treatments |
|---|