Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search
文献类型：期刊论文
| 作者 | Li, Xiuli1; Chen, Xinjian2; Yao, Jianhua2; Zhang, Xing1; Yang, Fei1; Tian, Jian1 |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2012-10-01 |
| 卷号 | 31期号:10页码:1849-1860 |
| 关键词 | Implicit shape registration multiple surfaces graph searching physical constraints renal cortex segmentation |
| 英文摘要 | In this paper, we present an automatic renal cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of renal cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the renal cortex and renal column. Third, a renal cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (D-avg), and average symmetric rms surface distance (D-rms). The experimental results of DSC, OE, SVD, D-avg, and D-rms were 90.50% +/- 1.19% 4.38% +/- 3.93%, 2.37% +/- 1.72%, 0.14 mm +/- 0.09 mm, and 0.80 mm +/- 0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method. |
| 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] | KIDNEY SEGMENTATION ; CT IMAGES ; MODEL ; INFORMATION ; ALGORITHMS ; VOLUMETRY ; SOFTWARE ; MRI |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000310149700002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/4064]  |
| 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.Chinese Acad Sci, Intelligent Med Res Ctr, Inst Automat, Beijing 100190, Peoples R China 2.NIH, Ctr Clin, Radiol & Imaging Sci Dept, Bethesda, MD 20814 USA |
| 推荐引用方式 GB/T 7714 | Li, Xiuli,Chen, Xinjian,Yao, Jianhua,et al. Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2012,31(10):1849-1860. |
| APA | Li, Xiuli,Chen, Xinjian,Yao, Jianhua,Zhang, Xing,Yang, Fei,&Tian, Jian.(2012).Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search.IEEE TRANSACTIONS ON MEDICAL IMAGING,31(10),1849-1860. |
| MLA | Li, Xiuli,et al."Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search".IEEE TRANSACTIONS ON MEDICAL IMAGING 31.10(2012):1849-1860. |
入库方式： OAI收割
来源：自动化研究所
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