CT Segmentation of Dinosaur Fossils by Deep Learning

doi:10.3389/feart.2021.805271

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	CT Segmentation of Dinosaur Fossils by Deep Learning
	Yu, Congyu 1,2; Qin, Fangbo3 ; Li, Ying 3; Qin, Zichuan 4; Norell, Mark 2
发表期刊	FRONTIERS IN EARTH SCIENCE
	2022-01-27
卷号	9 页码:8
通讯作者	Yu, Congyu(cyu@amnh.org)
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.
关键词	deep learning CT segmentation fossil dinosaur
DOI	10.3389/feart.2021.805271
关键词[WOS]	NEURAL-NETWORKS ; GAME ; GO
收录类别	SCI
语种	英语
WOS研究方向	Geology
WOS类目	Geosciences, Multidisciplinary
WOS记录号	WOS:000753163800001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47609
专题	中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Yu, Congyu
作者单位	1.Columbia Univ, Dept Earth & Environm Sci, New York, NY 10027 USA 2.Amer Museum Nat Hist, Div Paleontol, New York, NY 10024 USA 3.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China 4.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]. 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.