Lam Ngo Fung Daniel, Sun Hongfei, Song Liming, Yang Dongrong, Zhi Shaohua, Ren Ge, Chou Pak Hei, Wan Shiu Bun Nelson, Wong Man Fung Esther, Chan King Kwong, Tsang Hoi Ching Hailey, Kong Feng-Ming Spring, Wáng Yì Xiáng J, Qin Jing, Chan Lawrence Wing Chi, Ying Michael, Cai Jing
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China.
Quant Imaging Med Surg. 2022 Jul;12(7):3917-3931. doi: 10.21037/qims-21-791.
Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs.
Two bone suppression methods (Gusarev and Rajaraman ) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam).
Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance.
Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.
2019冠状病毒病（COVID-19）是一种大流行病。通过胸部X光片快速准确地诊断COVID-19，可实现稀缺医疗资源的更有效分配，从而改善患者预后。胸部X光片的深度学习分类可能是朝着这一目标迈出的合理一步。我们假设胸部X光片的去骨处理可能会提高胸部X光片中COVID-19现象深度学习分类的性能。
实施了两种去骨方法（古萨廖夫法和拉贾拉曼法）。古萨廖夫法和拉贾拉曼法是在来自X射线骨影抑制数据集（https://www.kaggle.com/hmchuong/xray-bone-shadow-supression）的217对正常和去骨胸部X光片上进行训练的。实施了两种具有不同网络架构的分类器方法。二元分类器模型在公开的RICORD-1c和RSNA肺炎挑战赛数据集上进行训练。从中国香港伊利沙伯医院的320例COVID-19阳性患者和中国香港东区尤德夫人那打素医院的518例非COVID-19患者中回顾性创建了一个外部测试数据集，并用于评估去骨处理对分类器性能的影响。将未进行去骨处理的X光片与进行了去骨处理的X光片在分类性能（通过灵敏度、特异性、阴性预测值（NPV）、准确率和受试者操作特征曲线下面积（AUC）进行量化）方面进行比较。本研究中使用的一些预训练模型发表于（https://github.com/danielnflam）。
发现对外部测试数据进行去骨处理可显著（P<0.05）提高一种分类器架构的AUC [从0.698（未去骨）提高到0.732（拉贾拉曼法去骨）]。对于另一种分类器架构，去骨处理并未显著（P>0.05）提高或降低分类器性能。
拉贾拉曼法去骨在一种分类架构中显著提高了分类性能，在另一种分类架构中也未显著降低分类器性能。本研究可扩展以探索去骨处理对不同肺部病变分类的影响，以及其他图像增强技术对分类器性能的影响。
