Automated segmentation of grey matter (GM) and white matter (WM) in gigapixel histopathology images is advantageous to analyzing distributions of disease pathologies, further aiding in neuropathologic deep phenotyping. Although supervised deep learning methods have shown good performance, its requirement of a large amount of labeled data may not be cost-effective for large scale projects. In the case of GM/WM segmentation, trained experts need to carefully trace the delineation in gigapixel images. To minimize manual labeling, we consider semi-surprised learning (SSL) and deploy one state-of-the-art SSL method (FixMatch) on WSIs. Then we propose a two-stage scheme to further improve the performance of SSL: the first stage is a self-supervised module to train an encoder to learn the visual representations of unlabeled data, subsequently, this well-trained encoder will be an initialization of consistency loss-based SSL in the second stage. We test our method on Amyloid-β stained histopathology images and the results outperform FixMatch with the mean IoU score at around 2% by using 6,000 labeled tiles while over 10% by using only 600 labeled tiles from 2 WSIs.Clinical relevance- this work minimizes the required labeling efforts by trained personnel. An improved GM/WM segmentation method could further aid in the study of brain diseases, such as Alzheimer's disease.