  Fast and accurate catheter detection in cardiac catheterization using
harmless 3D ultrasound (US) can improve the efficiency and outcome of the
intervention. However, the low image quality of US requires extra training for
sonographers to localize the catheter. In this paper, we propose a catheter
detection method based on a pre-trained VGG network, which exploits 3D
information through re-organized cross-sections to segment the catheter by a
shared fully convolutional network (FCN), which is called a Direction-Fused FCN
(DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized
by model fitting. Our experiments show that the proposed method can
successfully detect an ablation catheter in a challenging ex-vivo 3D US
dataset, which was collected on the porcine heart. Extensive analysis shows
that the proposed method achieves a Dice score of 57.7%, which offers at least
an 11.8 % improvement when compared to state-of-the-art instrument detection
methods. Due to the improved segmentation performance by the DF-FCN, the
catheter can be localized with an error of only 1.4 mm.
