Guidewire detection is an important and challenging problem in image-guided interventions. The guidewire is barely visible in fluoroscopic sequences, since it is thin and the image has poor quality. Most recent methods for guidewire localization have a first level of pixel-wise detection based on a trained classifier on hand-crafted features. A Convolutional Neural Network (CNN) could in principle learn its own features, however training a CNN for guidewire detection has proved to be difficult because the wire is very thin and can have any orientation. In this paper we present a method to train a Fully Convolutional Neural Network for guidewire detection, and highlight what challenges are encountered during training for this particular problem. We also introduce the Spherical Quadrature Filters (SQF) for guidewire detection and show how they can be used to improve the training data. Experiments show that the trained CNN outperforms many popular approaches such as the Frangi filter, the SQF and a trained classifier based on hand-crafted feature. Furthermore, we observe that a CNN approach that uses the SQF to obtain better aligned training examples further improves the detection accuracy.