Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

TL;DR

This paper presents a real-time, fully automatic deep learning-based method for segmenting catheters and guidewires in 2D X-ray fluoroscopy sequences, aiding in medical guidance and intervention.

Contribution

It introduces a novel deep convolutional neural network approach that segments instruments in real-time using minimal annotated data and data augmentation.

Findings

Median centerline distance error of 0.2 mm

Median tip distance error of 0.9 mm

Real-time automatic segmentation achieved

Abstract

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.