Guide-wire detection using region proposal network for X-ray image-guided navigation
Wang, Li; Xie, Xiao-Liang; Bian, Gui-Bin; Hou, Zeng-Guang; Cheng, Xiao-Ran; Prasong, Pusit
2017
会议名称Neural Networks (IJCNN), 2017 International Joint Conference on
会议日期2017
会议地点Anchorage Alaska USA
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

文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19997
专题复杂系统管理与控制国家重点实验室_先进机器人
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],2017.
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