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Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach
Song, Jiangdian1,2; Yang, Caiyun2; Fan, Li3; Wang, Kun2; Yang, Feng4; Liu, Shiyuan3; Tian, Jie2; Caiyun YANG, Shiyuan Liu, Jie Tian
2016
发表期刊IEEE TRANSACTIONS ON MEDICAL IMAGING
卷号35期号:1页码:337-353
文章类型Article
摘要The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic segmentation approach (TBGA) with a three-step framework, which are automatic initial seed point selection, multi-constraints 3D lesion extraction and the final lesion refinement. The new approach does not require any human interaction or training dataset for lesion detection, yet it can provide a high lesion detection sensitivity (96.35%) and a comparable segmentation accuracy with manual segmentation (P > 0.05), which was proved by a series assessments using the LIDC-IDRI dataset (850 lesions) and in-house clinical dataset (121 lesions). We also compared TBGA with commonly used level set and skeleton graph cut methods, respectively. The results indicated a significant improvement of segmentation accuracy (P < 0.05). Furthermore, the average time consumption for one lesion segmentation was under 8 s using our new method. In conclusion, we believe that the novel TBGA can achieve robust, efficient and accurate lung lesion segmentation in CT images automatically.
关键词Back-off Mechanism Computed Tomography (Ct) Lung Lesion Segmentation Region Growing Toboggan
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1109/TMI.2015.2474119
关键词[WOS]COMPUTED-TOMOGRAPHY IMAGES ; CT IMAGES ; DETECTION ALGORITHM ; NODULE DETECTION ; HELICAL CT ; LEVEL ; SCANS ; REGISTRATION ; SURFACE ; MODELS
收录类别SCI
语种英语
项目资助者Chinese Academy of Sciences Key Deployment Program(KGZD-EW-T03) ; National Basic Research Program of China (973 Program)(2011CB707700) ; National Natural Science Foundation of China(81227901 ; Biomedicine Department of Shanghai Science and Technology Commission(13411950100) ; Chinese Academy of Sciences(2013Y1GB0005 ; National High Technology Research and Development Program of China (863 Program)(2012AA021105) ; Guangdong Province-Chinese Academy of Sciences(2010A090100032 ; NSFC-NIH(81261120414) ; National Science and Technology Supporting Plan(2012BAI15B08) ; Beijing Natural Science Foundation(4132080) ; Fundamental Research Funds for the Central Universities(2013JBZ014) ; 61231004 ; 2010T2G36) ; 2012B090400039) ; 81370035 ; 81230030 ; 61301002 ; 61302025)
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000367624800029
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10658
专题中国科学院分子影像重点实验室
通讯作者Caiyun YANG, Shiyuan Liu, Jie Tian
作者单位1.Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Shenyang 110819, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Second Mil Med Univ, Changzheng Hosp, Dept Radiol, Shanghai 200003, Peoples R China
4.Beijing Jiaotong Univ, Beijing 100044, Peoples R China
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GB/T 7714
Song, Jiangdian,Yang, Caiyun,Fan, Li,et al. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2016,35(1):337-353.
APA Song, Jiangdian.,Yang, Caiyun.,Fan, Li.,Wang, Kun.,Yang, Feng.,...&Caiyun YANG, Shiyuan Liu, Jie Tian.(2016).Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach.IEEE TRANSACTIONS ON MEDICAL IMAGING,35(1),337-353.
MLA Song, Jiangdian,et al."Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach".IEEE TRANSACTIONS ON MEDICAL IMAGING 35.1(2016):337-353.
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