Effort-Free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-Body Micro-Ct Scans
説明
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.
5 pages, 5 figures, accepted to ICIP 2021
収録刊行物
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- 2021 IEEE International Conference on Image Processing (ICIP)
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2021 IEEE International Conference on Image Processing (ICIP) 279-283, 2021-09-19
IEEE
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キーワード
- FOS: Computer and information sciences
- Computer Science - Machine Learning
- Computer Vision and Pattern Recognition (cs.CV)
- Image and Video Processing (eess.IV)
- Computer Science - Computer Vision and Pattern Recognition
- FOS: Electrical engineering, electronic engineering, information engineering
- Electrical Engineering and Systems Science - Image and Video Processing
- Machine Learning (cs.LG)
詳細情報 詳細情報について
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- CRID
- 1870302167794209536
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- データソース種別
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- OpenAIRE
