Uncertain Feature-refinement Attention Unet: Considering Suitable Convolutional Neural Network Model for Real-time Segmentation in Markerless Tumor Tracking.

Purpose: In markerless tumor tracking (MTT) with deep learning, model performance suffers from domain shifts due to noise and anatomical changes. This study aimed to develop a convolutional neural network (CNN) model for real-time MTT segmentation. Methods: An Uncertain Feature-refinement Attention Unet (UFA-Unet), designed based on insights into CNN behavior under domain distribution shifts that occur between digitally reconstructed radiographs (DRRs) and kV X-ray fluoroscopic (XF) images, is proposed. A qualitative ablation study was performed to examine the contribution of each UFA-Unet component to segmentation accuracy. The model feasibility of UFA-Unet was evaluated through quantitative and phantom studies. The quantitative study included ten lung cancer cases, each containing two datasets (1 Results: The ablation study confirmed that each component suppressed over-activation to improve segmentation accuracy. In the quantitative study, UFA-Unet maintained superior performance compared with conventional models on both 1 Conclusion: UFA-Unet provides accurate, robust, and real-time segmentation, thus demonstrating its suitability for clinical MTT.