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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks
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An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks

机译：深神经网络胸部X射线肺分割与重建的自动化方法

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摘要

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.
机译：背景和目的：胸部X射线（CXR）是用于检测和诊断肺病的最常用的成像技术之一。任何计算机辅助系统中的关键组件，用于数字CXR中的检测或诊断，是肺域的自动分割。该任务所固有的主要挑战是在分段中包括致密异常重叠的肺区，也称为不透明度，这可能是由结核病和肺炎等疾病引起的。这种特定的任务是困难的，因为不透明性频繁地达到高强度值，这可以通过自动方法作为肺边界来解释，因此，这在分割过程中产生了挑战，因为不完全分割的可能性很大增加。本作作品的目的是提出一种通过在肺部异常重建肺区“丢失”来解决这种问题的CXR中肺部的自动分割方法。

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