CT Segmentation of Dinosaur Fossils by Deep Learning
文献类型：期刊论文
| 作者 | Yu, Congyu3,4; Qin, Fangbo2 ; Li, Ying2; Qin, Zichuan1; Norell, Mark3
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| 刊名 | FRONTIERS IN EARTH SCIENCE
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| 出版日期 | 2022-01-27 |
| 卷号 | 9页码:8 |
| 关键词 | deep learning CT segmentation fossil dinosaur |
| DOI | 10.3389/feart.2021.805271 |
| 通讯作者 | Yu, Congyu(cyu@amnh.org) |
| 英文摘要 | Recently, deep learning has reached significant advancements in various image-related tasks, particularly in medical sciences. Deep neural networks have been used to facilitate diagnosing medical images generated from various observation techniques including CT (computed tomography) scans. As a non-destructive 3D imaging technique, CT scan has also been widely used in paleontological research, which provides the solid foundation for taxon identification, comparative anatomy, functional morphology, etc. However, the labeling and segmentation of CT images are often laborious, prone to error, and subject to researchers own judgements. It is essential to set a benchmark in CT imaging processing of fossils and reduce the time cost from manual processing. Since fossils from the same localities usually share similar sedimentary environments, we constructed a dataset comprising CT slices of protoceratopsian dinosaurs from the Gobi Desert, Mongolia. Here we tested the fossil segmentation performances of U-net, a classic deep neural network for image segmentation, and constructed a modified DeepLab v3+ network, which included MobileNet v1 as feature extractor and practiced an atrous convolutional method that can capture features from various scales. The results show that deep neural network can efficiently segment protoceratopsian dinosaur fossils, which can save significant time from current manual segmentation. But further test on a dataset generated by other vertebrate fossils, even from similar localities, is largely limited. |
| WOS关键词 | NEURAL-NETWORKS ; GAME ; GO |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000753163800001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47608]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Yu, Congyu |
| 作者单位 | 1.Univ Bristol, Sch Earth Sci, Bristol, Avon, England 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China 3.Amer Museum Nat Hist, Div Paleontol, New York, NY 10024 USA 4.Columbia Univ, Dept Earth & Environm Sci, New York, NY 10027 USA |
| 推荐引用方式 GB/T 7714 | Yu, Congyu,Qin, Fangbo,Li, Ying,et al. CT Segmentation of Dinosaur Fossils by Deep Learning[J]. FRONTIERS IN EARTH SCIENCE,2022,9:8. |
| APA | Yu, Congyu,Qin, Fangbo,Li, Ying,Qin, Zichuan,&Norell, Mark.(2022).CT Segmentation of Dinosaur Fossils by Deep Learning.FRONTIERS IN EARTH SCIENCE,9,8. |
| MLA | Yu, Congyu,et al."CT Segmentation of Dinosaur Fossils by Deep Learning".FRONTIERS IN EARTH SCIENCE 9(2022):8. |
入库方式： OAI收割
来源：自动化研究所
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