Catheter detection in 3D ultrasound using triplanar-based convolutional neural networks

3D Ultrasound (US) image-based catheter detection can potentially decrease the cost on extra equipment and training. Meanwhile, accurate catheter detection enables to decrease the operation duration and improves its outcome. In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanar-based CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. When compared to state-of-the-art handcrafted features on ex-vivo datasets, our proposed method improves the F2-score with at least 31%. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images.