title: Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain.
abstract: Suppression of bony structures in chest radiographs (CXRs) is potentially useful for radiologists and computer-aided diagnostic schemes. In this paper, we present an effective deep learning method for bone suppression in single conventional CXR using deep convolutional neural networks (ConvNets) as basic prediction units. The deep ConvNets were adapted to learn the mapping between the gradients of the CXRs and the corresponding bone images. We propose a cascade architecture of ConvNets (called CamsNet) to refine progressively the predicted bone gradients in which the ConvNets work at successively increased resolutions. The predicted bone gradients at different scales from the CamsNet are fused in a maximum-a-posteriori framework to produce the final estimation of a bone image. This estimation of a bone image is subtracted from the original CXR to produce a soft-tissue image in which the bone components are eliminated. Our method was evaluated on a dataset that consisted of 504 cases of real two-exposure dual-energy subtraction chest radiographs (404 cases for training and 100 cases for test). The results demonstrate that our method can produce high-quality and high-resolution bone and soft-tissue images. The average relative mean absolute error of the produced bone images and peak signal-to-noise ratio of the produced soft-tissue images were 3.83% and 38.7dB, respectively. The average bone suppression ratio of our method was 83.8% for the CXRs with pixel sizes of nearly 0.194mm. Furthermore, we apply the trained CamsNet model on the CXRs acquired by various types of X-ray machines, including scanned films, and our method can also produce visually appealing bone and soft-tissue images.
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Wei
authors: W
authors: Yang
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Yingyin
authors: Y
authors: Chen
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Yunbi
authors: Y
authors: Liu
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Liming
authors: L
authors: Zhong
authors: Radiology Department, Nanfang Hospital, Southern Medical University, Guangzhou, China.
authors: Genggeng
authors: G
authors: Qin
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Zhentai
authors: Z
authors: Lu
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.
authors: Qianjin
authors: Q
authors: Feng
authors: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors: Wufan
authors: W
authors: Chen
mesh_terms: D000465:Algorithms; D001842:Bone and Bones; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D013902:Radiography, Thoracic / Q000379:methods*
keywords: Bone suppression; Chest radiography; Convolutional neural network; Dual-energy subtraction