  Machine Learning-based fast and quantitative automated screening plays a key
role in analyzing human bones on Computed Tomography (CT) scans. However,
despite the requirement in drug safety assessment, such research is rare on
animal fetus micro-CT scans due to its laborious data collection and
annotation. Therefore, we propose various bone feature engineering techniques
to thoroughly automate the skeletal localization/labeling/abnormality detection
of rat fetuses on whole-body micro-CT scans with minimum effort. Despite
limited training data of 49 fetuses, in skeletal labeling and abnormality
detection, we achieve accuracy of 0.900 and 0.810, respectively.
