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The lung airway tree modeling is essential to work for the diagnosis ofpulmonary diseases, especially for X-Ray computed tomography (CT). The airwaytree modeling on CT images can provide the experts with 3-dimensionmeasurements like wall thickness, etc. This information can tremendously aidthe diagnosis of pulmonary diseases like chronic obstructive pulmonary disease[1-4]. Many scholars have attempted various ways to model the lung airway tree,which can be split into two major categories based on its nature. Namely, themodel-based approach and the deep learning approach. The performance of atypical model-based approach usually depends on the manual tuning of the modelparameter, which can be its advantages and disadvantages. The advantage is itsdon't require a large amount of training data which can be beneficial for asmall dataset like medical imaging. On the other hand, the performance ofmodel-based may be a misconcep-tion [5,6].  In recent years, deep learning has achieved good results in the field ofmedical image processing, and many scholars have used UNet-based methods inmedical image segmentation [7-11]. Among all the variation of UNet, the UNet 3+[11] have relatively good result compare to the rest of the variation of UNet.Therefor to further improve the accuracy of lung airway tree modeling, thisstudy combines the Frangi filter [5] with UNet 3+ [11] to develop adual-channel 3D UNet 3+. The Frangi filter is used to extracting vessel-likefeature. The vessel-like feature then used as input to guide the dual-channelUNet 3+ training and testing procedures.

Airway Tree Modeling Using Dual-channel 3D UNet 3+ with Vesselness Prior