Amyloid-beta plaque quantification and analysis
C. de Vries (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marcel J T Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
X. Zhang – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
T. Höllt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
J.J.M. Hoozemans – Graduation committee member (Vrije Universiteit Amsterdam)
S. Rhode – Graduation committee member (Vrije Universiteit Amsterdam)
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Abstract
Alzheimer's disease (AD) is becoming more prevalent as the world population gets older. The formation of Amyloid-beta (\AB) plaques is one of the pathologies related to AD. Recent work has shown that the \ab load in brain tissue has a negative correlation with cognitive performance in cognitively healthy centenarians.
This work aims to expand this research by investigating whether the types of \ab plaque present are linked to cognition and by comparing the types of plaques in the centenarian cohort with an AD cohort.
For this task, a system is developed that can identify \ab plaques in images of brain tissue. It first automatically segments the grey matter using a fine-tuned U-net. Then the plaques are located using traditional image processing techniques. Lastly, shape and size features are extracted from the plaque in addition to a feature vector made by a pre-trained AlexNet. K-means clustering is used on AlexNet features to find categories for the plaques.
The clustering approach failed to yield good results. However, the area and roundness are differently distributed between the AD and centenarian cohorts, but the differences are small. Correlations have been found between the area and roundness of plaques in the occipital pole and cognitive performance in centenarians. They indicate that cognitively stronger individuals have smaller and less round \ab plaques in their brains. More research is necessary to reveal the true extent of the impact of plaque types on cognition.