Segmentation of lung airways based on deep learning methods

Abstract

Abstract Precise segmentation of the lung airways is essential for a quantitative assessment of airway diseases. However, because of the complexity of the airway structure and the different thicknesses of the trachea at different positions, it is extremely difficult to segment the fine bronchial structure using chest computed tomography (CT). Traditional lung airway segmentation methods are generally based on the grayscale, geometric shape of the image, or the use of prior knowledge of anatomy. In recent years, deep learning techniques such as fully convolutional neural networks (FCNs) have achieved a great success in the field of image segmentation. Specifically, the symmetric encoder–decoder network represented by U‐Net has achieved high accuracy in many medical image segmentation tasks. In the airway segmentation challenge task of the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020), 9 of the 12 teams participating in the final round used the U‐Net network or one of its other forms, obtaining good results for lung airway segmentation. Methods used to improve the segmentation accuracy include attention mechanisms and multiscale feature information fusion. This article provides a detailed description of the methods used by these 12 teams and analyses their results.