Semantic Segmentation of Eight Regions of Upper and Lower Limb Bones Using 3D U-Net in Whole-body CT Images
Author:
Affiliation:
1. Graduate School of Information Science and Technology, Aichi Prefectural University
2. Faculty of Engineering, Gifu University
3. Center for Healthcare Information Technology(C-HiT), Tokai National Higher Education and Research System
Publisher
Japanese Society of Radiological Technology
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jjrt/76/11/76_2020_JSRT_76.11.1125/_pdf
Reference7 articles.
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2. 2) Kamiya N, Zhou X, Chen H, et al. Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol Phys Technol 2012; 5(1): 5-14.
3. 3) Kamiya N, Oshima A, Asano E, et al. Initial study on the classification of amyotrophic diseases using texture analysis and deep learning in whole-body CT images. Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500X. doi: https://doi.org/10.1117/12.2518199.
4. 4) Bieth M, Peter L, Nekolla SG, et al. Segmentation of skeleton and organs in whole-body CT images via iterative trilateration. IEEE Trans Med Imaging 2017; 36(11): 2276-2286.
5. 5) Belal SL, Sadik M, Kaboteh R, et al. Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol 2019; 113: 89-95.
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