Autoencoder-based bone removal algorithm from x-ray images of the lung

Abstract

The application of machine learning methods in biomedical image analysis has recently become of particular interest to researchers. One of the most common diagnostic methods with low cost and high availability is X-ray imaging. It allows the acquisition of frontal images of the chest, which can be used in the medical diagnosis of various diseases and prognosis. Due to the presence of ribs on the image, some pathologic changes may go unnoticed. The goal of this work is to develop a method, using deep learning techniques, to remove ribs from chest X-ray images. The Bone Suppression dataset, consisting of 35 pairs of standard X-ray and soft-tissue only images, was used to develop the model. COVIDx was used as an external test set. Due to the small number of images in the training cohort, a data augmentation technique was used to generate new, noisy image pairs. A deep learning model using convolutional denoising autoencoder architecture was developed to remove the ribs from the X-ray image. The effects of two image down-sampling methods and learning rate changes were evaluated. The resulting images are characterized by partial or complete suppression of the ribs. It should be noted that the problem was not posed by images of patients suffering from COVID-19, which are characterized by much more complex structures.