Feasibility study of catheter segmentation in 3D Frustum ultrasounds by DCNN

Nowadays, 3D ultrasound (US) has been employed rapidly in medical intervention therapies, such as cardiac catheterization. To efficiently interpret 3D US images and localize the catheter during the surgery, an experienced sonographer is required. As a consequence, image-based catheter detection can be a benefit to sonographer to localize the instrument in the 3D US images timely. Conventionally, the 3D imaging methods are based on the Cartesian domain, which is limited by bandwidth and information lose when it is converted from the original acquisition space-Frustum domain. The exploration of catheter segmentation in Frustum space helps to reduce the computational cost and improve efficiency. In this paper, we present a catheter segmentation method in 3D Frustum image via a deep convolutional network (DCNN). To better describe 3D information and reduce the complexity of DCNN, cross-planes with spatial gaps are extracted for each voxel. Then, the cross-planes of the voxel are processed by the DCNN to distinguish it, whether it is a catheter voxel or not. To accelerate the prediction efficiency on whole US Frustum volume, a filter-based pre-selection is applied to reduce the computational cost of the DCNN. Based on experiments on the ex-vivo dataset, our proposed method can segment the catheter in Frustum images with 0.67 Dice score within 3 seconds, which indicates the possibility of real-time application.