期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 35, 期 1, 页码 337-353出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2474119
关键词
Back-off mechanism; computed tomography (CT); lung lesion segmentation; region growing; toboggan
类别
资金
- 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, 61231004, 81370035, 81230030, 61301002, 61302025]
- Biomedicine Department of Shanghai Science and Technology Commission [13411950100]
- Chinese Academy of Sciences [2013Y1GB0005, 2010T2G36]
- National High Technology Research and Development Program of China (863 Program) [2012AA021105]
- Guangdong Province-Chinese Academy of Sciences [2010A090100032, 2012B090400039]
- NSFC-NIH [81261120414]
- National Science and Technology Supporting Plan [2012BAI15B08]
- Beijing Natural Science Foundation [4132080]
- Fundamental Research Funds for the Central Universities [2013JBZ014]
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.
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