Deep learning methods for extraction of neuroimage markers in the prognosis of brain pathologies

Clèrigues Garcia, Albert
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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 ​
​L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc/4.0/

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