2021/06/03 by Akihiro Fukuda, Fukuda, Akihiro, Changhee Han +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Metabolomics and Mass Spectrometry Studies #Molecular Biology Techniques and Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.01830
openalex publication_date 2021/06/03 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28
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.