Purpose

Airway tree segmentation plays a pivotal role in chest computed tomography (CT) analysis tasks such as lesion localization, surgical planning, and intra-operative guidance. The remaining challenge is to identify small bronchi correctly, which facilitates further segmentation of the pulmonary anatomies.

Methods

A three-dimensional (3D) multi-scale feature aggregation network (MFA-Net) is proposed against the scale difference of substructures in airway tree segmentation. In this model, the multi-scale feature aggregation (MFA) block is used to capture the multi-scale context information, which improves the sensitivity of the small bronchi segmentation and addresses the local discontinuities. Meanwhile, the concept of airway tree partition is introduced to evaluate the segmentation performance at a more granular level.

Results

Experiments were conducted on a dataset of 250 CT scans, which were annotated by experienced clinical radiologists. Through the airway partition, we evaluated the segmentation results of the small bronchi compared with the state-of-the-art methods. Experiments show that MFA-Net achieves the best performance in the Dice similarity coefficient (DSC) in the intra-lobar airway and improves the true positive rate (TPR) by 7.59% on average. Besides, in the entire airway, the proposed method achieves the best results in DSC and TPR scores of 86.18% and 79.31%, respectively, with the consequence of higher false positives.

Conclusion

The MFA-Net is competitive with the state-of-the-art methods. The experiment results indicate that the MFA block improves the performance of the network by utilizing multi-scale context information. More accurate segmentation results will be more helpful in further clinical analysis.

中文翻译：

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基于多尺度上下文信息的气道树自动分割

目的

气道树分割在胸部计算机断层扫描（CT）分析任务（例如病变定位，手术计划和术中指导）中起着关键作用。剩下的挑战是正确地识别小支气管，这有助于进一步细分肺部解剖结构。

方法

针对气道树分割中子结构的尺度差异，提出了一种三维（3D）多尺度特征聚合网络（MFA-Net）。在此模型中，多尺度特征聚合（MFA）块用于捕获多尺度上下文信息，从而提高了小支气管分割的敏感性并解决了局部不连续性。同时，引入了气道树分区的概念，以更细致地评估分割性能。

结果

实验是在250个CT扫描的数据集上进行的，并由经验丰富的临床放射科医生进行注释。通过气道分隔，我们与最新技术相比，评估了小支气管的分割结果。实验表明，MFA-Net在叶内气道的Dice相似系数（DSC）方面表现最佳，平均真实阳性率（TPR）平均提高7.59％。此外，在整个气道中，所提出的方法在DSC和TPR评分上分别达到86.18％和79.31％的最佳结果，其结果是假阳性率更高。

结论

MFA-Net与最先进的方法相比具有竞争力。实验结果表明，MFA块通过利用多尺度上下文信息提高了网络性能。更准确的分割结果将对进一步的临床分析有所帮助。