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A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images

文献类型：期刊论文

作者Jiang, Taijiao4,5,6; Wang, Kuansong1,2; Guo, Wei1,2; Guo, Zichao7; Wang, Xiangdong7; Su, Mingming3,4,5,6; Ni, Haomiao7; Liu, Hong7; Qian, Yueliang7
刊名SCIENTIFIC REPORTS
出版日期2019-01-29
卷号9页码:10
ISSN号2045-2322
DOI10.1038/s41598-018-37492-9
英文摘要Supervised learning methods are commonly applied in medical image analysis. However, the success of these approaches is highly dependent on the availability of large manually detailed annotated dataset. Thus an automatic refined segmentation of whole-slide image (WSI) is significant to alleviate the annotation workload of pathologists. But most of the current ways can only output a rough prediction of lesion areas and consume much time in each slide. In this paper, we propose a fast and refined cancer regions segmentation framework v3_DCNN, which first preselects tumor regions using a classification model Inception-v3 and then employs a semantic segmentation model DCNN for refined segmentation. Our framework can generate a dense likelihood heatmap with the 1/8 side of original WSI in 11.5 minutes on the Camelyon16 dataset, which saves more than one hour for each WSI compared with the initial DCNN model. Experimental results show that our approach achieves a higher FROC score 83.5% with the champion's method of Camelyon16 challenge 80.7%. Based on v3 DCNN model, we further automatically produce heatmap of WSI and extract polygons of lesion regions for doctors, which is very helpful for their pathological diagnosis, detailed annotation and thus contributes to developing a more powerful deep learning model.
资助项目Beijing Natural Science Foundation[4172058] ; Central Public-interest Scientific Institution Basal Research Fund[2016ZX310195] ; Central Public-interest Scientific Institution Basal Research Fund[2017PT31026]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000456955500062
出版者NATURE PUBLISHING GROUP
源URL[http://119.78.100.204/handle/2XEOYT63/3442]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Kuansong; Liu, Hong; Qian, Yueliang
作者单位1.Cent S Univ, Sch Basic Med Sci, Dept Pathol, Changsha 410013, Hunan, Peoples R China
2.Cent S Univ, Xiangya Hosp, Dept Pathol, Changsha 410013, Hunan, Peoples R China
3.Peking Union Med Coll, Grad Sch, Beijing 100005, Peoples R China
4.Suzhou Inst Syst Med, Suzhou 215123, Peoples R China
5.Peking Union Med Coll, Beijing 100005, Peoples R China
6.Chinese Acad Med Sci, Res Ctr Big Data Biomed Sci, Inst Basic Med Sci, Beijing 100005, Peoples R China
7.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Taijiao,Wang, Kuansong,Guo, Wei,et al. A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images[J]. SCIENTIFIC REPORTS,2019,9:10.
APA Jiang, Taijiao.,Wang, Kuansong.,Guo, Wei.,Guo, Zichao.,Wang, Xiangdong.,...&Qian, Yueliang.(2019).A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images.SCIENTIFIC REPORTS,9,10.
MLA Jiang, Taijiao,et al."A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images".SCIENTIFIC REPORTS 9(2019):10.

入库方式： OAI收割

来源：计算技术研究所

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