Automatic Tuberculosis Screening Using Chest Radiographs
Automatic Tuberculosis Screening Using Chest Radiographs
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DOI：
10.1109/tmi.2013.2284099
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发表时间：
2014-02-01
影响因子：
10.6
通讯作者：
McDonald, Clement J.
中科院分区：
文献类型：
--
作者：
Jaeger, Stefan;Karargyris, Alexandros;McDonald, Clement J.
结核病是世界许多地区的主要健康威胁。免疫功能低下的艾滋病毒/艾滋病患者的机会性感染和多重耐药菌株加剧了这一问题，而诊断结核病仍然是一个挑战。如果未得到诊断和治疗，结核病患者的死亡率很高。标准诊断仍然依赖于上世纪开发的方法。它们很慢而且常常不可靠。为了减轻疾病负担，本文介绍了我们在传统后前位胸片中检测结核病的自动化方法。我们首先使用图割分割方法提取肺部区域。对于这个肺部区域，我们计算一组纹理和形状特征，这使得能够使用二元分类器将 X 射线分类为正常或异常。我们在两个数据集上衡量我们系统的性能：一组由美国当地县卫生部门结核病控制项目收集，一组由中国深圳医院收集。拟议的结核病筛查计算机辅助诊断系统已准备好进行现场部署，其性能接近人类专家的性能。第一组的 ROC 曲线下面积 (AUC) 为 87%（准确度为 78.3%），第二组的 AUC 为 90%（准确度为 84%）。对于第一组，我们将系统性能与放射科医生的性能进行比较。当试图不漏掉任何阳性病例时，放射科医生在这组数据上的准确率约为 82%，他们的误报率约为我们系统误报率的一半。
Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.