  The lung airway tree modeling is essential to work for the diagnosis of
pulmonary diseases, especially for X-Ray computed tomography (CT). The airway
tree modeling on CT images can provide the experts with 3-dimension
measurements like wall thickness, etc. This information can tremendously aid
the 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, the
model-based approach and the deep learning approach. The performance of a
typical model-based approach usually depends on the manual tuning of the model
parameter, which can be its advantages and disadvantages. The advantage is its
don't require a large amount of training data which can be beneficial for a
small dataset like medical imaging. On the other hand, the performance of
model-based may be a misconcep-tion [5,6].
  In recent years, deep learning has achieved good results in the field of
medical image processing, and many scholars have used UNet-based methods in
medical 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, this
study combines the Frangi filter [5] with UNet 3+ [11] to develop a
dual-channel 3D UNet 3+. The Frangi filter is used to extracting vessel-like
feature. The vessel-like feature then used as input to guide the dual-channel
UNet 3+ training and testing procedures.
