Efficient Catheter Segmentation in 3D Cardiac Ultrasound using Slice-Based FCN With Deep Supervision and F-Score Loss
Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With · 2019
Fast and accurate catheter segmentation in 3D ultrasound (US) can improve the outcome and efficiency of cardiac interventions. In this paper, we propose an efficient catheter segmentation method based on a fully convolutional neural network (FCN). The FCN is based on a pre-trained VGG-16 model, which processes the 3D US volumes slice by slice. To enhance its performance, we modify its structure by skipping connections under a deep supervision structure, which is learned with an F-score loss function. Our method can exploit more contextual information and increase the detection of catheter-like voxels. We collected a challenging ex-vivo dataset (92 3D US images) from porcine hearts with an RF-ablation catheter inside. Our experiments on this dataset show that the proposed method achieves a segmentation performance with an F2score of 65.2% with a highly efficient inference around 1.1 sec. per volume.