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

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.

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