An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks
An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks
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DOI：
10.1016/j.cmpb.2019.06.005
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发表时间：
2019-08-01
影响因子：
6.1
通讯作者：
de Paiva, Anselmo Cardoso
中科院分区：
文献类型：
--
作者：
Souza, Johnatan Carvalho;Bandeira Diniz, Joao Otavio;de Paiva, Anselmo Cardoso
背景与目的：胸部X线检查（CXR）是肺部疾病最常用的影像学检查方法之一。在任何计算机辅助系统中，用于数字CXR中的检测或诊断的关键组件是肺野的自动分割。该任务固有的主要挑战之一是在分割中包括被密集异常（也称为不透明）重叠的肺区域，其可以由诸如结核和肺炎之类的疾病引起。该特定任务是困难的，因为不透明度经常达到高强度值，其可以被自动方法错误地解释为肺边界，并且因此，这在分割过程中产生了挑战，因为不完整分割的机会显著增加。这项工作的目的是提出一种用于在CXR中自动分割肺部的方法，该方法通过重建由于肺部异常而“丢失”的肺部区域来解决这个问题。方法：所提出的方法具有两个深度卷积神经网络模型，包括四个主要步骤：（1）图像采集，（2）初始分割，（3）重建和（4）最终分割。对蒙哥马利县结核病控制项目的138例胸部X线图像进行了实验，取得了平均灵敏度97.54%，平均特异度96.79%，平均准确度96.97%，平均Dice系数94%，平均Jaccard指数88.07%的最佳结果。我们在我们的肺部分割方法中证明，胸部X射线中的密集异常问题可以通过基于深度卷积神经网络模型执行重建步骤来有效解决。(C)2019爱思唯尔B. V.保留所有权利。
Background and Objective: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the automatic segmentation of the lung field. One of the main challenges inherent to this task is to include in the segmentation the lung regions overlapped by dense abnormalities, also known as opacities, which can be caused by diseases such as tuberculosis and pneumonia. This specific task is difficult because opacities frequently reach high intensity values which can be incorrectly interpreted by an automatic method as the lung boundary, and as a consequence, this creates a challenge in the segmentation process, because the chances of incomplete segmentations are increased considerably. The purpose of this work is to propose a method for automatic segmentation of lungs in CXR that addresses this problem by reconstructing the lung regions "lost" due to pulmonary abnormalities.Methods: The proposed method, which features two deep convolutional neural network models, consists of four steps main steps: (1) image acquisition, (2) initial segmentation, (3) reconstruction and (4) final segmentation.Results: The proposed method was experimented on 138 Chest X-ray images from Montgomery County's Tuberculosis Control Program, and has achieved as best result an average sensitivity of 97.54%, an average specificity of 96.79%, an average accuracy of 96.97%, an average Dice coefficient of 94%, and an average Jaccard index of 88.07%.Conclusions: We demonstrate in our lung segmentation method that the problem of dense abnormalities in Chest X-rays can be efficiently addressed by performing a reconstruction step based on a deep convolutional neural network model. (C) 2019 Elsevier B.V. All rights reserved.