An automatic framework for estimating the pose of the catheter distal section using a coarse-to-fine network

During percutaneous coronary intervention procedures, generally only 2D X-ray images are provided. The consequent lack of depth perception makes it difficult for interventionists to visually estimate the pose of medical tools inside the vasculature, especially for novices. Although some automatic methods have been developed to aid interventionists, it is still a challenging task to obtain stable and accurate pose estimation. In this paper, we describe a learning-based framework for estimating the pose of the catheter distal section (CDS). The main innovation of this framework is the proposal of a coarse-to-fine fusion network (CFF-Net) which can achieve the shape and orientation estimation for the CDS.

By adopting a two-step fusion, CFF-Net progressively solves the shape and orientation ambiguities. The first step is the early fusion where the 2D projection image fuses with the shape prior before input, which makes the estimated result own a specific catheter distal shape. The second step is the late fusion where CFF-Net fuse feature maps and the orientation data from Electromagnetic (EM) sensors to confirm the overall orientation of the CDS. Finally, the estimated pose in the EM space will be obtained after we combine the estimated shape and orientation from CFF-Net with the position information from the EM sensor.

The effectiveness of CFF-Net has been verified in a simulated environment where RMSE of CFF-Net is 0.706 ± 0.121 mm. This approach was further transferred from simulation to reality using the real-world data, where RMSE of CFF-Net is 1.121 ± 0.124 mm and RMSE of the whole proposed framework is 1.577 ± 0.144 mm.

In simulated and real-world experiments, our proposed approach has been proven to achieve high accuracy while ensuring real-time processing for estimating the pose of the CDS.