An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis

by   Di Xu, et al.

Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. Nevertheless, with physical models able to preserve morphological details but at the cost of extremely long processing time, existing DL methods face challenges of gathering sufficient/qualitative ground truth (GT) for robust training, thus leading to failure in maintaining clinically acceptable false positive rates. We hereby propose a generalizable yet efficient workflow of two stages: (1) training pairs generation with GT bone shadows eliminated in by a physical model in spatially transformed gradient fields. (2) fully supervised image denoising network training on stage-one datasets for fast rib removal on incoming CXRs. For step two, we designed a densely connected network called SADXNet, combined with peak signal to noise ratio and multi-scale structure similarity index measure objective minimization to suppress bony structures. The SADXNet organizes spatial filters in U shape (e.g., X=7; filters = 16, 64, 256, 512, 256, 64, 16) and preserves the feature map dimension throughout the network flow. Visually, SADXNet can suppress the rib edge and that near the lung wall/vertebra without jeopardizing the vessel/abnormality conspicuity. Quantitively, it achieves RMSE of  0 during testing with one prediction taking <1s. Downstream tasks including lung nodule detection as well as common lung disease classification and localization are used to evaluate our proposed rib suppression mechanism. We observed 3.23 6.62 positive decrease for lung nodule detection and common lung disease localization, separately.


page 5

page 9

page 10

page 11

page 14


Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is ...

Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images

A chest radiograph, commonly called chest x-ray (CxR), plays a vital rol...

X-ray Dissectography Improves Lung Nodule Detection

Although radiographs are the most frequently used worldwide due to their...

GAN-based disentanglement learning for chest X-ray rib suppression

Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can ...

Rib Suppression in Digital Chest Tomosynthesis

Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D...

A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning

Computer-aided diagnosis (CAD) techniques for lung field segmentation fr...

Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

Given image labels as the only supervisory signal, we focus on harvestin...

Please sign up or login with your details

Forgot password? Click here to reset