论文标题
使用无镜头显微镜图像预测结核病的自动语义分割
Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
论文作者
论文摘要
结核病（TB）是由一种称为结核分枝杆菌的细菌引起的，是秘鲁和世界上最严重的公共卫生问题之一。该项目的开发旨在通过MODS方法和使用无镜头显微镜来促进和自动化结核病的诊断，这与镜头显微镜相比，它们更易于校准，并且更易于使用（未经训练的人员）。因此，我们在收集的数据集中采用U-NET网络来执行TB绳索的自动分割，以预测结核病。我们的最初结果显示了自动分割结核病线的有希望的证据。
Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for automatic segmentation of TB cords.
