Krishnan Chetana, Hussain Shah, Stanford Denise, Sthanam Venkata, Bodduluri Sandeep, Raju S Vamsee, Rowe Steven M, Kim Harrison
Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
Department of Pulmonary, Allergy, and Critical Care, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
J Imaging Inform Med. 2025 May 22. doi: 10.1007/s10278-025-01545-z.
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.
准确的气道分割对于肺部疾病的诊断和管理至关重要，但由于数据不平衡和难以检测小气道分支，仍然具有挑战性。本研究提出了AirSeg，这是一个可学习的互连注意力框架，结合了先进的注意力机制和一个可学习的嵌入模块，以提高计算机断层扫描（CT）图像中的气道分割准确性。所提出的框架集成了多种注意力机制，包括图像、位置、语义、自通道和跨空间注意力，以在不同的网络和数据级别上细化特征表示。此外，一个基于可学习方差的嵌入模块动态调整输入特征，提高对空间不一致性和噪声的鲁棒性。这提高了模型对空间不一致性和噪声的鲁棒性，从而产生更可靠的分割结果，特别是在临床具有挑战性的区域。AirSeg可以灵活地与任何类似UNet的网络集成。该框架在两个数据集（体内和原位）上使用了几种基于UNet的架构进行评估，比较了集成AirSeg和未集成AirSeg时的性能。训练采用了数据增强、结合Dice相似系数和交并比损失的混合损失函数以及统计分析来评估准确性的提高。将AirSeg集成到分割模型中导致准确性有统计学意义的提高。具体而言，体内数据集的准确性提高了16.18%（p = 0.0035），原位数据集的准确性提高了10.32%（p = 0.0097）。这些改进使得能够更精确地识别气道结构，包括小分支，这对于肺部护理中的早期诊断和治疗计划至关重要。所提出的模型比其他传统模型实现了12.43%（p = 0.0004）的加权平均准确性提高。AirSeg在捕捉全局结构和精细细节方面表现出卓越性能，有效地分割了大气道和复杂分支。消融研究验证了各个注意力机制和嵌入模块的贡献和影响。准确性的提高转化为更精确的气道分割，增强了对早期诊断和治疗计划至关重要的小分支的检测。具有统计学意义的p值证实了这些增益是可靠的，减少了人工校正工作并提高了临床环境中自动气道分析的效率。
