论文标题
改进的基于U-NET结构和形态操作的肺部细分
Improved lung segmentation based on U-Net architecture and morphological operations
论文作者
论文摘要
胸部X射线的计算机辅助诊断的基本阶段是自动肺部分割。由于肋骨笼子和每个人肺的独特方式，必须构建有效的自动肺部分割模型。本文提出了一个可靠的模型，用于分割胸部X光片中的肺部。我们的模型通过学习忽略源胸部X光片中不重要的区域并强调肺部分割的重要特征来克服挑战。我们在公共数据集，蒙哥马利和深圳评估了模型。提出的模型的骰子系数为98.1％，这证明了我们模型的可靠性。
An essential stage in computer aided diagnosis of chest X rays is automated lung segmentation. Due to rib cages and the unique modalities of each persons lungs, it is essential to construct an effective automated lung segmentation model. This paper presents a reliable model for the segmentation of lungs in chest radiographs. Our model overcomes the challenges by learning to ignore unimportant areas in the source Chest Radiograph and emphasize important features for lung segmentation. We evaluate our model on public datasets, Montgomery and Shenzhen. The proposed model has a DICE coefficient of 98.1 percent which demonstrates the reliability of our model.
