论文标题
带有RES-CR-NET的胸部X射线肺部分割
Lung Segmentation in Chest X-rays with Res-CR-Net
论文作者
论文摘要
深神经网络（DNN）被广泛用于在生物医学图像中执行分割任务。为此目的而开发的大多数DNN都是基于编码器u-net体系结构的一些变体。在这里，我们表明，RES-CR-NET是一种新型的全卷积神经网络，该网络最初是为了显微镜图像的语义分割而开发的，并且不采用U-NET结构，在健康患者或患有多种肺部病理学的患者的胸部X射线中的肺场中非常有效。
Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.
