  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% and
6.62% area under the curve (AUC) increase as well as 203 and 385 absolute false
positive decrease for lung nodule detection and common lung disease
localization, separately.
