Research On Bone Suppression Of Chest Radiograph

Chest radiography(CXR)is widely used in routine examination of lung diseases for its cheapness and safety.However,the interpretation of lung lesions is often interfered by the bone structures,such as ribs and clavicle.Suppression of bones in chest radiography would be potentially useful for radiologists as well as computer-aided system.This article introduces a method which uses dual-energy subtraction data to train the convolution neural network,learning the mapping between chest radiography and bone structures.Then the statistic model is used to suppress the bone structure in chest radiography.This method belongs to a virtual dual-energy subtraction technique.In this article,we first introduce the basic neural network.The gradient maps of chest radiograph are set as the network’s input,while the gradient maps of chest radiograph minus tissue radiograph are set as the network’s label data,the network is trained by Adam algorithm.Aiming at the problem of long training time of convolutional network,by replacing the large convolution kernel into smaller one and decreasing the size of output features,we have successfully reduce the network’s training time and the running time.Based on the basic convolutional network,we have discussed the influence of different network parameters and network structures on the network’s accuracy and running time.The experimental results manifest that convolution network works well both on quantitatively metrics and visual perception.According to the shortage of basic fixed patch size network,we have trained a multi-resolution convolutional network,which integrates the features at different resolution,to avoid the problem of limited-information of fixed patch size.Compared to the basic convolution network,the multi-resolution convolution network has a significant improvement in quantitative metrics.