Guide-wire detection using region proposal network for X-ray image-guided navigation
文献类型：会议论文
| 作者 | Wang, Li; Xie, Xiao-Liang ; Bian, Gui-Bin; Hou, Zeng-Guang; Cheng, Xiao-Ran; Prasong, Pusit
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| 出版日期 | 2017 |
| 会议日期 | 2017 |
| 会议地点 | Anchorage Alaska USA |
| 英文摘要 | Detection of surgical devices, in particular of guidewire detection, is prerequisite during image-guided navigation in percutaneous coronary intervention (PCI). Guide-wire detection is a challenging task for following reasons: (i) X-ray images have a low signal-to-noise rate (SNR); (ii) there is a high similarity between guide-wires and some other adjacent anatomical skeletons’ contours; (iii) guide-wires have various shapes and their motion is complex and nonlinear. Traditionally, guide-wires are detected using curve fitting method, and third-order B-spline curve model is always used to fit guide-wires, while B-spline fitting method has some obvious shortcomings such as it is a semi-automatic method which needs manual initialization, and it is not a real-time method because of high computational complexity. Recently, with the availability of large annotated datasets and the accessibility of hardware resources with GPUs, it is succeeded in detecting general objects with convolutional neural networks (ConvNet). In this paper, we present a novel image-based fully-automatic and real-time approach with ConvNet for guide-wires detection. ConvNet method is robust to guide-wires’ various poses and other structures’ effects. We evaluate our method on 22 different sequences of X-ray images. The detection accuracy evaluated by average precision (AP) reaches 89.2% and the detection speed achieves 40fps. Our experiment result shows a promising for accurate and real-time guide-wires detection in PCI navigation with ConvNet model |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19997]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 推荐引用方式 GB/T 7714 | Wang, Li,Xie, Xiao-Liang,Bian, Gui-Bin,et al. Guide-wire detection using region proposal network for X-ray image-guided navigation[C]. 见:. Anchorage Alaska USA. 2017. |
入库方式： OAI收割
来源：自动化研究所
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