Papers › A segmentation method of airway from chest CT image based on Vgg-Unet neural network
A segmentation method of airway from chest CT image based on Vgg-Unet neural network
Wei Shao; Zhe Wang; Ziming Zhang; Qinghua Zhou; Ruoyu Wang; Wenjun Tan
Segmenting the pulmonary airway in CT images to obtain its anatomical structure information can help doctors better understand the patient’s condition and determine the treatment plan. However, due to the blurred boundary of the pulmonary airway in CT images, it has a high degree of overlap with the surrounding pulmonary parenchyma and other tissues, making segmentation difficult. In this paper, a method based on the separation of pulmonary airway in CT images is investigated. Based on the original U-net network, using the idea of migration learning, the U-net and VGG16 network with partially similar network structure are fused to form a VGG-Unet joint neural network, which not only accelerates the model training and image segmentation efficiency, but also ensures the accuracy of image segmentation. The experimental data source used CT images acquired from the partner hospital. The algorithm in this paper was used to segment and extract the CT images of airway of lungs with an accuracy of 86.52%, and the segmentation efficiency was high and stable.
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