B110-08 An Attention-Enhanced U-Net Neural Network Model for Airway Segmentation in Expiratory Computed Tomography Images

Abstract Rationale This study introduces a new segmentation model for expiratory CT images by integrating a novel attention gate with a U-Net framework to improve airway continuity and completeness. Methods We analyzed data from 120 subjects across four cohorts: asthma, COPD, post-COVID-19, and healthy controls. Each cohort includes 25 subjects with residual volume (RV) scans, which were used for training, validation, and independent testing. We proposed a 3D segmentation framework incorporating an attention gate model, termed Averaged Multi-Gaussian Response (AMGR), integrating with the U-Net architecture, specifically optimized for expiratory airway segmentation. The proposed model was compared against two state-of-the-art methods, which are Fuzzy Attention Neural Network (FANN) and standard U-Net, to evaluate its performance. Quantitative assessments were conducted using Dice, Precision, and Recall metrics. Results The proposed model achieved a Dice score of 0.9629. It outperformed two baseline models: FANN (0.9084) and U-Net (0.9477). In terms of Precision, the proposed model had 0.9621, indicating superior segmentation performance compared with FANN (0.8370) and U-Net (0.9397). While FANN reported the highest Recall (0.9947), the proposed model showed a balanced trade-off between Precision and Recall (0.9647). Conclusion The AMGR approach stabilizes feature fusion by suppressing peripheral noise and enhancing distal airway continuity. The proposed model achieved a high Dice score, while maintaining balanced Precision and Recall for the multi-disease cohort. Overall, the model demonstrated robust noise suppression and improved airway continuity in the RV scans. This abstract is funded by: NIH