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Localizing tuberculosis in chest radiographs with deep learning

机译：通过深度学习在胸部X光片中定位结核病

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Chest radiography (CXR) has been used as an effective tool for screening tuberculosis (TB). Because of the lack of radiological expertise in resource-constrained regions, automatic analysis of CXR is appealing as a "first reader'. In addition to screening the CXR for disease, it is critical to highlight locations of the disease in abnormal CXRs. In this paper, we focus on the task of locating TB in CXRs which is more challenging due to the intrinsic difficulty of locating the abnormality. The method is based on applying a convolutional neural network (CNN) to classify the superpixels generated from the lung area. Specifically, it consists of four major components: lung ROI extraction, superpixel segmentation, multi-scale patch generation/labeling, and patch classification. The TB regions are located by identifying those superpixels whose corresponding patches are classified as abnormal by the CNN. The method is tested on a publicly available TB CXR dataset which contains 336 TB images showing various manifestations of TB. The TB regions in the images were marked by radiologists. To evaluate the method, the images are split into training, validation, and test sets with all the manifestations being represented in each set. The performance is evaluated at both the patch level and image level. The classification accuracy on the patch test set is 72.8% and the average Dice index for the test images is 0.67. The factors that may contribute to misclassification are discussed and directions for future work are addressed.
机译：胸部放射线照相（CXR）已被用作筛查肺结核（TB）的有效工具。由于在资源有限的地区缺乏放射学专业知识，因此对CXR的自动分析作为“第一读者”颇具吸引力，除了对CXR进行疾病筛查外，突出显示异常CXR中疾病的位置也很重要。在本文中，我们将重点放在在CXR中定位TB的任务，该任务由于定位异常的内在困难而更具挑战性，该方法基于应用卷积神经网络（CNN）对从肺区域生成的超像素进行分类。 ，它由四个主要部分组成：肺ROI提取，超像素分割，多尺度斑块生成/标记和斑块分类，通过识别CNN将其相应斑块分类为异常的那些超像素来定位TB区域。在公开可用的TB CXR数据集上进行了测试，该数据集包含336个显示结核病各种表现的TB图像，图像中的TB区域标有放射性标记ts。为了评估该方法，将图像分为训练集，验证集和测试集，并在每组中表示所有表现形式。在补丁程序级别和映像级别都对性能进行了评估。斑块测试集上的分类精度为72.8％，测试图像的平均Dice指数为0.67。讨论了可能导致分类错误的因素，并讨论了未来工作的方向。

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