Fully automatic multi-organ segmentation based on multi-boost learning and statistical shape model search
文献类型：会议论文
| 作者 | Baochun He; Cheng Huang; Fucang Jia |
| 出版日期 | 2015 |
| 会议名称 | ISBI 2015 VISCERAL Anatomy for Grand Challenge |
| 会议地点 | New York, USA |
| 英文摘要 | In this paper, an automatic multi-organ segmentation based on multi-boost learning and statistical shape model search was proposed. First, simple but robust Multi-Boost Classifier was trained to hierarchically locate and pre-segment multiple organs. To ensure the generalization ability of the classier relative location information between organs, organ and whole body is exploited. Left lung and right lung are first localized and pre-segmented, then liver and spleen are detected upon its location in whole body and its relative location to lungs, kidney is finally detected upon the features of relative location to liver and left lung. Second, shape and appearance models are constructed for model tting. The final refinement delineation is performed by best point searching guided by appearance pro le classi er and is constrained with multi-boost classi ed probabilities, intensity and gradient features. The method was tested on 30 unseen CT and 30 unseen enhanced CT (CTce) datasets from ISBI 2015 VISCERAL challenge. The results demonstrated that the multi-boost learning can be used to locate multi-organ robustly and segment lung and kidney accurately. The liver and spleen segmentation based on statistical shape searching has shown good performance too. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/7227]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Baochun He,Cheng Huang,Fucang Jia. Fully automatic multi-organ segmentation based on multi-boost learning and statistical shape model search[C]. 见:ISBI 2015 VISCERAL Anatomy for Grand Challenge. New York, USA. |
入库方式： OAI收割
来源：深圳先进技术研究院
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