  It is common in anthropology and paleontology to address questions about
extant and extinct species through the quantification of osteological features
observable in micro-computed tomographic (micro-CT) scans. In cases where
remains were buried, the grey values present in these scans may be classified
as belonging to air, dirt, or bone. While various intensity-based methods have
been proposed to segment scans into these classes, it is often the case that
intensity values for dirt and bone are nearly indistinguishable. In these
instances, scientists resort to laborious manual segmentation, which does not
scale well in practice when a large number of scans are to be analyzed. Here we
present a new domain-enriched network for three-class image segmentation, which
utilizes the domain knowledge of experts familiar with manually segmenting bone
and dirt structures. More precisely, our novel structure consists of two
components: 1) a representation network trained on special samples based on
newly designed custom loss terms, which extracts discriminative bone and dirt
features, 2) and a segmentation network that leverages these extracted
discriminative features. These two parts are jointly trained in order to
optimize the segmentation performance. A comparison of our network to that of
the current state-of-the-art U-NETs demonstrates the benefits of our proposal,
particularly when the number of labeled training images are limited, which is
invariably the case for micro-CT segmentation.
