Guide-wire detection using region proposal network for X-ray image-guided navigation
Li Wang, Xiao‐Liang Xie, Gui‐Bin Bian, Zeng‐Guang Hou, Xiao-Ran Cheng, Pusit Prasong · 2017
Detection of surgical devices, in particular of guide-wire 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.