Støverud Karen-Helene, Bouget David, Pedersen André, Leira Håkon Olav, Amundsen Tore, Langø Thomas, Hofstad Erlend Fagertun
Department of Health Research, SINTEF, Trondheim, Norway.
Sopra Steria, Application Solutions, Trondheim, Norway.
PLoS One. 2024 Oct 2;19(10):e0311416. doi: 10.1371/journal.pone.0311416. eCollection 2024.
To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available.
为改善患有肺部疾病（如肺癌）患者的预后，早期诊断和治疗至关重要。CT图像分析对诊断具有重要价值，而气道树的高质量分割对于支气管镜检查期间的干预规划和实时引导是必需的。最近，多域气道树建模（ATM'22）挑战赛发布了一个大型数据集，既能够训练基于深度学习的模型，又能在气道分割任务上大幅提升当前的技术水平。ATM'22数据集包括一大组新冠肺炎患者以及一系列其他肺部疾病患者，然而，发现患有影响气道树解剖结构的严重病变的患者相对较少。在本研究中，我们引入了一个新的公共基准数据集（AeroPath），它由27张来自患有从肺气肿到大型肿瘤等各种病变患者的CT图像组成，并带有相应的气管和支气管标注。其次，我们提出了一种用于自动气道分割的多尺度融合设计。模型在ATM'22数据集上进行训练，在AeroPath数据集上进行测试，并与有竞争力的开源方法进行进一步评估。使用与ATM'22挑战赛相同的性能指标来对不同的考虑方法进行基准测试。最后，开发了一个开放的网络应用程序，以便在新数据上轻松测试所提出的模型。结果表明，我们提出的架构为AeroPath数据集中包含的所有患者预测了拓扑正确的分割结果。所提出的方法具有鲁棒性，能够处理各种异常情况，至少到第五级气道。此外，以患有具有挑战性病变的患者为特色的AeroPath数据集将有助于新的先进方法的开发。AeroPath数据集和网络应用程序已公开提供。
