Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) within neurons. The distribution and density of tau pathology throughout the brain is evaluated at autopsy as one component of Alzheimers disease diagnosis. Deep neural networks (DNN) have been shown to be effective in the quantification of tau pathology when trained on fully annotated images. In this paper, we examine the effectiveness of three DNNs for the segmentation of tau pathology when trained on noisily labeled data. We train FCN, SegNet and U-Net on the same set of training images. Our results show that using noisily labeled data, these networks are capable of segmenting tau pathology as well as nuclei in as few as 40 training epochs with varying degrees of success. SegNet, FCN and U-Net are able to achieve a DICE loss of 0.234, 0.297 and 0.272 respectively on the task of segmenting regions of tau. We also apply these networks to the task of segmenting whole slide images of tissue sections and discuss their practical applicability for processing gigapixel sized images.