The computational detection of lung lobes from computed tomography images is a challenging segmentation problem with important respiratory healthcare applications, including emphysema, chronic bronchitis, and asthma. This paper proposes a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. First, our model performs automated segmentation of the lung lobes in a progressive random forest network, eliminating the need for prior segmentation of lungs, vessels, or airways. Then, an interactive lobes segmentation approach based on random walk mechanism is designed for improving auto-segmentation accuracy. Furthermore, we annotate a new dataset which contains 93 scans (57 men, 36 women; age range: 40–90 years) from the Central Hospital Affiliated with Shenyang Medical College (CHASMC). We evaluate the model on our annotated dataset, LIDC (https://wiki.cancerimagingarchive.net) and LOLA11 (http://lolall.com/) datasets. The proposed model achieved a Dice score of \(0.906 \pm 0.106\) for LIDC, \(0.898 \pm 0.113\) for LOLA11, and \(0.921 \pm 0.101\) for our dataset. Experimental results show the accuracy of the proposed approach, which consistently improves performance across different datasets by a maximum of 8.2% as compared to baselines model.

中文翻译：

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PRF-RW：基于渐进式随机森林的随机游动方法，用于交互式半自动肺叶分割

从计算机断层扫描图像对肺叶进行计算检测是重要的呼吸系统保健应用（包括肺气肿，慢性支气管炎和哮喘）中一个具有挑战性的分割问题。本文提出了一种基于交互林的基于随机森林的随机游动方法，用于交互式半自动肺叶分割。首先，我们的模型在渐进式随机森林网络中对肺叶进行自动分割，从而无需事先对肺，血管或气道进行分割。然后，设计了一种基于随机游走机制的交互式波瓣分割方法，以提高自动分割的准确性。此外，我们注释了一个新的数据集，其中包含沉阳医学院附属中央医院（CHASMC）的93幅扫描图（男57例，女36例；年龄范围：40-90岁）。我们在带注释的数据集LIDC（https://wiki.cancerimagingarchive.net）和LOLA11（http://lolall.com/）数据集上评估模型。提出的模型的Dice得分为对于LIDC为（0.906 \ pm 0.106 \），对于LOLA11为\（0.898 \ pm 0.113 \），对于我们的数据集为\（0.921 \ pm 0.101 \）。实验结果表明了该方法的准确性，与基线模型相比，该方法在不同数据集上的性能始终提高了8.2％。