Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities like COVID-19 may become difficult due to that they are obscured by the presence of bony structures like the ribs and the clavicles, thereby resulting in screening/diagnostic misinterpretations. Automated bone suppression methods would help suppress these bony structures and increase soft tissue visibility. In this study, we propose to build an ensemble of convolutional neural network models to suppress bones in frontal CXRs, improve classification performance, and reduce interpretation errors related to COVID-19 detection. The ensemble is constructed by (i) measuring the multi-scale structural similarity index (MS-SSIM) score between the sub-blocks of the bone-suppressed image predicted by each of the top-3 performing bone-suppression models and the corresponding sub-blocks of its respective ground truth soft-tissue image, and (ii) performing a majority voting of the MS-SSIM score computed in each sub-block to identify the sub-block with the maximum MS-SSIM score and use it in constructing the final bone-suppressed image. We empirically determine the sub-block size that delivers superior bone suppression performance. It is observed that the bone suppression model ensemble outperformed the individual models in terms of MS-SSIM and other metrics. A CXR modality-specific classification model is retrained and evaluated on the non-bone-suppressed and bone-suppressed images to classify them as showing normal lungs or other COVID-19-like manifestations. We observed that the bone-suppressed model training significantly outperformed the model trained on non-bone-suppressed images toward detecting COVID-19 manifestations.
翻译:X射线(CXR)是一种广泛进行的放射检查,有助于检测组织和器官在胸腔中的异常。检测COVID-19这样的肺异常可能变得很困难,因为它们被肋骨和颈部等骨质结构所掩盖,从而导致筛选/诊断错误。自动化骨质抑制方法将有助于抑制这些骨质结构,提高软组织能见度。在本研究中,我们提议建立一个卷变神经网络模型的集合,以压制前部CXR的骨骼,改进分类性能,并减少与COVID-19检测有关的解释错误。该元素的构建方式是:(一)测量骨质结构相似性指数(MS-SSIM)在骨质抑制图像小块之间的评分点。由进行骨质抑制模型的每个顶层-骨质压模型和相应的地面软质素图像的分级分类,以及(二)在SISSB的骨质压模型中进行最高多数选举,在SISDMIM的骨质压模型中,通过SBS-S-SBS的亚分级分析,在SIM的骨质压中,通过SIS-Scial-de-de-de-deal-de-deal Stal-de-de-de-de-deal Stal-de-deal deal deal deal destral ex ex ex deal deal 将Smaxxxxxxxxxx在每部的骨质压演示的骨质压演示演示的骨质表现显示,在每部的骨质演示的骨质压模型中,在SBres。