一种提高肺部异常诊断的胸部X射线肋骨抑制的有效和鲁棒方法
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis
摘要：
抑制胸部X线片（CXR）上的胸部骨影已被证明可提高肺部疾病的诊断。以前的方法可以分为无监督的物理和监督的深度学习模型。然而，由于物理模型能够保留形态学细节，但以极长的处理时间为代价，现有的DL方法面临着收集足够/定性的地面实况（GT）进行鲁棒训练的挑战，从而导致无法维持临床上可接受的假阳性率。我们在此提出了两个阶段的可推广但有效的工作流程：（1）在空间变换的梯度场中通过物理模型消除GT骨阴影的训练对生成。(2)在第一阶段数据集上进行全监督图像去噪网络训练，以快速去除传入CXR上的肋骨。对于第二步，我们设计了一个称为SADXNet的密集连接网络，结合峰值信噪比和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形组织空间滤波器（例如，X=7;过滤器= 16，64，256，512，256，64，16），并在整个网络流中保留特征图维度。在视觉上，SADXNet可以抑制肋骨边缘和肺壁/椎骨附近的边缘，而不会损害血管/异常的显著性。定量地，它在测试期间实现了~0的RMSE，其中一个预测需要<1 s。下游任务包括肺结节检测以及常见肺部疾病分类和定位，用于评估我们提出的肋骨抑制机制。我们分别观察到肺结节检测和常见肺部疾病定位的曲线下面积（AUC）增加3.23%和6.62%，以及绝对假阳性减少203和385。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
