Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach
文献类型：期刊论文
| 作者 | Song, Jiangdian1,2; Yang, Caiyun2 ; Fan, Li3; Wang, Kun2; Yang, Feng4; Liu, Shiyuan3; Tian, Jie2
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| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
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| 出版日期 | 2016 |
| 卷号 | 35期号:1页码:337-353 |
| 关键词 | Back-off mechanism computed tomography (CT) lung lesion segmentation region growing toboggan |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 研究领域[WOS] | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 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 |
| WOS记录号 | WOS:000367624800029 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10658]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 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 |
| 推荐引用方式 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.,...&Tian, Jie.(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. |
入库方式： OAI收割
来源：自动化研究所
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