# AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation

## TL;DR

AirSeg is a new framework that improves the accuracy of airway segmentation in CT scans, helping with early diagnosis and treatment of lung diseases.

## Contribution

AirSeg introduces a learnable interconnected attention framework with multiple attention mechanisms and a dynamic embedding module for robust airway segmentation.

## Key findings

- AirSeg improved accuracy by 16.18% in in vivo datasets and 10.32% in in situ datasets.
- The model achieved a 12.43% weighted average accuracy improvement over conventional models.
- Ablation studies confirmed the effectiveness of individual attention mechanisms and the embedding module.

## Abstract

Accurate airway segmentation is vital for diagnosing and managing lung diseases, yet it remains challenging due to data imbalance and difficulty detecting small airway branches. This study proposes AirSeg, a learnable interconnected attention framework incorporating advanced attention mechanisms and a learnable embedding module, to enhance airway segmentation accuracy in computed tomography (CT) images. The proposed framework integrates multiple attention mechanisms, including image, positional, semantic, self-channel, and cross-spatial attention, to refine feature representations at various network and data levels. Additionally, a learnable variance-based embedding module dynamically adjusts input features, improving robustness against spatial inconsistencies and noise. This improves the model’s robustness to spatial inconsistencies and noise, leading to more reliable segmentation results, especially in clinically challenging regions. AirSeg can be integrated with any UNet-like network with flexibility. The framework was evaluated on two datasets (in vivo and in situ) using several UNet-based architectures, comparing performance with and without AirSeg integration. Training employed data augmentation, a hybrid loss function combining Dice Similarity Coefficient and Intersection over Union losses, and statistical analysis to assess accuracy improvements. Integrating AirSeg into segmentation models led to statistically significant improvements in accuracy. Specifically, accuracy increased by 16.18% (p = 0.0035) for in vivo datasets and by 10.32% (p = 0.0097) for in situ datasets. These enhancements enable more precise identification of airway structures, including small branches, critical for early diagnosis and treatment planning in pulmonary care. The proposed model achieved a weighted average accuracy improvement of 12.43% (p = 0.0004) over other conventional models. AirSeg demonstrated superior performance in capturing both global structures and fine details, effectively segmenting large airways and intricate branches. Ablation studies validated the contributions and impact of individual attention mechanisms and the embedding module. The improvement in accuracy translates to more precise airway segmentation, enhancing the detection of small branches crucial for early diagnosis and treatment planning. The statistically significant p-values confirm that these gains are reliable, reducing manual correction efforts and improving the efficiency of automated airway analysis in clinical settings.