  Grain segmentation of sandstone that is partitioning the grain from its
surrounding matrix/cement in the thin section is the primary step for
computer-aided mineral identification and sandstone classification. The
microscopic images of sandstone contain many mineral grains and their
surrounding matrix/cement. The distinction between adjacent grains and the
matrix is often ambiguous, making grain segmentation difficult. Various
solutions exist in literature to handle these problems; however, they are not
robust against sandstone petrography's varied pattern. In this paper, we
formulate grain segmentation as a pixel-wise two-class (i.e., grain and
background) semantic segmentation task. We develop a deep learning-based
end-to-end trainable framework named Deep Semantic Grain Segmentation network
(DSGSN), a data-driven method, and provide a generic solution. As per the
authors' knowledge, this is the first work where the deep neural network is
explored to solve the grain segmentation problem. Extensive experiments on
microscopic images highlight that our method obtains better segmentation
accuracy than various segmentation architectures with more parameters.
