Training a Steerable CNN for Guidewire Detection

Guidewires are thin wires used in coronary angioplasty to guide different tools to access and repair the obstructed artery. The whole procedure is monitored using fluoroscopic (real-time X-ray) images. Due to the guidewire being thin in the low quality fluoroscopic images, it is usually poorly visible. The poor quality of the X-ray images makes the guidewire detection a challenging problem in image-guided interventions. Localizing the guidewire could help in enhancing its visibility and for other automatic procedures. Guidewire localization methods usually contain a first step of computing a pixelwise guidewire response map on the entire image. In this paper, we present a steerable Convolutional Neural Network (CNN), which is a Fully Convolutional Neural Network (FCNN) that can detect objects rotated by an arbitrary 2D angle, without being rotation invariant. In fact, the steerable CNN has an angle parameter that can be changed to make it sensitive to objects rotated by that angle. We present an application of this idea to detecting the guidewire pixels, and compare it with an FCNN trained to be invariant to the guidewire orientation. Results reveal that the proposed method is a good choice, outperforming some popular filter-based and learning-based approaches such as Frangi Filter, Spherical Quadrature Filter, FCNN and a state of the art trained classifier based on hand-crafted feature.