CASIA OpenIR  > 中国科学院分子影像重点实验室
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
AbstractThe 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.
KeywordBack-off Mechanism Computed Tomography (Ct) Lung Lesion Segmentation Region Growing Toboggan
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationChinese 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 Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000367624800029
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Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorCaiyun YANG, Shiyuan Liu, Jie Tian
Affiliation1.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
Recommended Citation
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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