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.

翻译：胸部X光片（CXRs）中胸骨阴影的抑制已被证明能改善肺部疾病的诊断。现有方法可分为无监督物理模型与有监督深度学习模型两大类。然而，物理模型虽能保留形态学细节，但需耗费极长的处理时间；而现有深度学习方法在获取足够且高质量的标注真值（GT）进行鲁棒训练时面临挑战，导致难以维持临床可接受的假阳性率。本文提出了一种兼具普适性与高效性的两阶段工作流：（1）通过物理模型在空间变换梯度场中消除GT骨阴影，生成训练配对数据；（2）在第一阶段数据集上训练全监督图像去噪网络，对输入的胸部X光片实现快速肋骨移除。第二阶段中，我们设计了一种名为SADXNet的密集连接网络，结合峰值信噪比与多尺度结构相似性指数测度目标最小化来抑制骨骼结构。SADXNet以U形结构组织空间滤波器（例如X=7；滤波器通道数依次为16、64、256、512、256、64、16），并在网络流程中保持特征图维度不变。视觉上，SADXNet可在不损害血管/异常组织可见度的前提下抑制肋骨边缘及靠近肺壁/椎体的区域。量化指标显示，测试期间均方根误差（RMSE）趋近于0，单次预测时长<1秒。我们采用肺结节检测、常见肺部疾病分类与定位等下游任务评估所提肋骨抑制机制：在肺结节检测与常见肺部疾病定位任务中，曲线下面积（AUC）分别提升3.23%和6.62%，绝对假阳性数分别减少203例和385例。