Identifying Catheter and Line Position in Chest X-Rays Using GANs

Abstract

Catheter is a thin tube that is inserted into patients body to provide fluids or medication. The placement of catheter in the chest is very important and if placed wrongly can be life threatening. Radiologists utilize X-ray images of the chest to determine the correctness of placement of catheter. In the time of global pandemic, when the hospitals are crowded with the patients, radiologists might not be able to manually observe all the X-rays. In this situation, an automatic method to identify catheter in the X-ray images would be of great help. In this paper, a novel method to automatically detect the presence and position of the catheter using X-ray images is developed. The proposed algorithm deploys generative adversarial network (GAN) to synthesize the catheter in X-ray images. Transfer learning is then used to classify the catheter and its correct placement. Octave convolution instead of vanilla convolution is utilized to improve the efficiency of deep learning method for classification. Through data augmentation different transformation of images are generated to make the model more robust to noisy images.