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

Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans