Catheter Tracking Using a Convolutional Neural Network for Decreasing Interventional Radiology X-Ray Exposure

Introduction

Although the many advantages of Interventional Radiology not only being a minimally invasive surgery but also providing minimal risk of infection for the patient, this procedure could cause serious damage (radio dermatitis) to the patient and surgeons if exposed for long periods to the X-ray radiation. Some medical solutions have been found, but need the installation of extra equipment in the operating room.

Objectives

The aim of the Medic@ team is to reduce the doses of X-rays using sensors integrated into the catheter to reconstruct images without the need of continuous imaging. To do that, accurate and reliable information on the position of the catheter is required to correct the drift of the catheter's sensors. The use of artificial intelligence with a U-Net convolutional neural network is a possible solution for detecting the entire catheter (body and head) and for obtaining precise coordinates in X-ray images.

Material and methods

The use of artificial intelligence with a U-Net convolutional neural network is a possible solution for detecting the entire catheter (body and head) and for obtaining precise coordinates in X-ray images. We have created and used synthetic data to generate training datasets and videos that simulate real-world operations because we only have low quantity of data.

Results

The results using the metrics binary cross entropy and dice loss testing in the synthetic data are 0. 048 and 0.98 respectively. We have also tested to predict catheter shapes on some real images; in a general way, the results show good approximation in the detection of the head of the catheter (around 3.1 pixels) using Euclidean distance. Finally, the predictions are also robust in blurry synthetic images using 5, 10 and 15 kernel sizes; in this case, the binary cross entropy in all the cases is less than 0.05 and the dice loss in all the cases is more than 0.98.

Conclusions

The methodology used to create synthetic images and videos seems to be correct. The predictions in the detection of the shape of catheters, after training with synthetic images calibrated with the same histogram of the real images, show very good results in the metrics: binary cross entropy and dice loss. The same for the case of blurry images. The tests in the few real images are encouraging because the error detection in the head of the catheter is small (<3.1 pixels). More tests with real data are still necessary for validating this first approach.