用自训练框架提升慢阻肺患者肺血管分割精度
A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD
- 采用教师-学生模型迭代生成可靠伪标签，实现半监督分割
- 在125例患者数据上精度达90.3%，提升2.3%
- 适用于慢阻肺不同严重程度的血管变化分析
背景：在慢性阻塞性肺疾病（COPD）患者中，准确分割和量化肺部CT图像中的肺血管（尤其是小血管）至关重要。目的：提出一种半监督方法用于肺血管分割。方法：构建基于教师-学生模型的自训练框架。首先通过交互方式获取内部数据集中的高质量标注；随后，在少量标注数据上训练全监督模型作为教师模型；利用该教师模型为未标注图像生成伪标签，并基于特定策略筛选出可靠的伪标签；学生模型则结合这些伪标签进行训练。该过程迭代进行直至性能最优。结果：在125例非增强CT扫描数据上进行大量实验，定量与定性分析表明，所提方法Semi2将血管分割精度提升了2.3%，达到90.3%。进一步对COPD患者肺血管的定量分析揭示了不同疾病严重程度下的血管差异。结论：该方法不仅提升了肺血管分割性能，还可用于COPD分析。代码将开源至https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation。
原文摘要 · Abstract (English)
Background: It is fundamental for accurate segmentation and quantification of the pulmonary vessel, particularly smaller vessels, from computed tomography (CT) images in chronic obstructive pulmonary disease (COPD) patients. Objective: The aim of this study was to segment the pulmonary vasculature using a semi-supervised method. Methods: In this study, a self-training framework is proposed by leveraging a teacher-student model for the segmentation of pulmonary vessels. First, the high-quality annotations are acquired in the in-house data by an interactive way. Then, the model is trained in the semi-supervised way. A fully supervised model is trained on a small set of labeled CT images, yielding the teacher model. Following this, the teacher model is used to generate pseudo-labels for the unlabeled CT images, from which reliable ones are selected based on a certain strategy. The training of the student model involves these reliable pseudo-labels. This training process is iteratively repeated until an optimal performance is achieved. Results: Extensive experiments are performed on non-enhanced CT scans of 125 COPD patients. Quantitative and qualitative analyses demonstrate that the proposed method, Semi2, significantly improves the precision of vessel segmentation by 2.3%, achieving a precision of 90.3%. Further, quantitative analysis is conducted in the pulmonary vessel of COPD, providing insights into the differences in the pulmonary vessel across different severity of the disease. Conclusion: The proposed method can not only improve the performance of pulmonary vascular segmentation, but can also be applied in COPD analysis. The code will be made available at https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation.
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