干旱区生态环境知识资源中心(Arid): CT Segmentation of Dinosaur Fossils by Deep Learning

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DOI	10.3389/feart.2021.805271
	CT Segmentation of Dinosaur Fossils by Deep Learning
	Yu, Congyu; Qin, Fangbo; Li, Ying; Qin, Zichuan; Norell, Mark
通讯作者	Yu, CY (corresponding author)，Columbia Univ, Dept Earth & Environm Sci, New York, NY 10027 USA. ; Yu, CY (corresponding author)，Amer Museum Nat Hist, Div Paleontol, New York, NY 10024 USA.
来源期刊	FRONTIERS IN EARTH SCIENCE
EISSN	2296-6463
出版年	2022
卷号	9
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.
英文关键词	deep learning CT segmentation fossil dinosaur
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000753163800001
WOS关键词	NEURAL-NETWORKS ; GAME ; GO
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376504
作者单位	[Yu, Congyu] Columbia Univ, Dept Earth & Environm Sci, New York, NY 10027 USA; [Yu, Congyu; Norell, Mark] Amer Museum Nat Hist, Div Paleontol, New York, NY 10024 USA; [Qin, Fangbo; Li, Ying] Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China; [Qin, Zichuan] Univ Bristol, Sch Earth Sci, Bristol, Avon, England
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],2022,9.
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.
MLA	Yu, Congyu,et al."CT Segmentation of Dinosaur Fossils by Deep Learning".FRONTIERS IN EARTH SCIENCE 9(2022).