Deep learning to segment pelvic bones: large-scale CT datasets and baseline models

doi:10.1007/s11548-021-02363-8

	Deep learning to segment pelvic bones: large-scale CT datasets and baseline models
	Liu, Pengbo 1; Han, Hu 1; Du, Yuanqi 3; Zhu, Heqin 1; Li, Yinhao 1; Gu, Feng 1,4; Xiao, Honghu 2; Li, Jun 1; Zhao, Chunpeng 2; Xiao, Li 1
刊名	INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
	2021-04-16
页码	8
关键词	CT dataset Pelvic segmentation Deep learning SDF post-processing
ISSN号	1861-6410
DOI	10.1007/s11548-021-02363-8
英文摘要	Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored. Methods: In this paper, we aim to bridge the data gap by curating a large pelvic CT dataset pooled from multiple sources, including 1184 CT volumes with a variety of appearance variations. Then, we propose for the first time, to the best of our knowledge, to learn a deep multi-class network for segmenting lumbar spine, sacrum, left hip, and right hip, from multiple-domain images simultaneously to obtain more effective and robust feature representations. Finally, we introduce a post-processor based on the signed distance function (SDF). Results: Extensive experiments on our dataset demonstrate the effectiveness of our automatic method, achieving an average Dice of 0.987 for a metal-free volume. SDF post-processor yields a decrease of 15.1% in Hausdorff distance compared with traditional post-processor. Conclusion: We believe this large-scale dataset will promote the development of the whole community and open source the images, annotations, codes, and trained baseline models at https://github.com/ICT-MIRACLE-lab/CTPelvic1K.
资助项目	Youth Innovation Promotion Association CAS[2018135]
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging ; Surgery
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000640763300001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16660]
专题	中国科学院计算技术研究所
通讯作者	Zhou, S. Kevin
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Beijing Jishuitan Hosp, Beijing, Peoples R China 3.George Mason Univ, Fairfax, VA 22030 USA 4.Beijing Elect Sci & Technol Inst, Beijing, Peoples R China 5.Univ Sci & Technol China, Sch Biomed Engn, Suzhou, Peoples R China 6.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China
推荐引用方式 GB/T 7714	Liu, Pengbo,Han, Hu,Du, Yuanqi,et al. Deep learning to segment pelvic bones: large-scale CT datasets and baseline models[J]. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY,2021:8.
APA	Liu, Pengbo.,Han, Hu.,Du, Yuanqi.,Zhu, Heqin.,Li, Yinhao.,...&Zhou, S. Kevin.(2021).Deep learning to segment pelvic bones: large-scale CT datasets and baseline models.INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY,8.
MLA	Liu, Pengbo,et al."Deep learning to segment pelvic bones: large-scale CT datasets and baseline models".INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2021):8.