Micro-computer tomography (μCT) is increasingly used in Anthropology and Palaeontology to quantify the external and internal osteological characteristics of extant/extinct species. One of the challenging tasks on such data is the accurate segmentation of images into bone and non-bone classes. Many intensity-based segmentation approaches have been proposed to overcome this issue, moving from global-thresholding to robust (semi)automatic methods. However, researchers often resort to laborious manual segmentation when the intensity levels of bone and non-bone material are extremely similar. Recently, deep learning methods have been shown to outperform traditional approaches for image segmentation. Here we propose a novel domain enriched deep network architecture that combines the benefits of deep learning with expert knowledge of bone structure via two components-1) a representation network capable of extracting features that are more responsive to bone structures and less responsive to non-bone structures, and 2) a segmentation network that utilizes the features obtained from the representation network to perform segmentation. Effective representation filters are obtained through a robust discriminative-features constraint that enables the discovery of novel features and enhances segmentation accuracy. Experiments performed on challenging μCT images of archaeological bones reveal practical merits of our proposal over state-of-the-art alternatives.