提出可同时精准分割增强与非增强肺部血管的3D算法。
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation
- 设计中心线优化模块与中心线Dice损失，提升血管结构稳定性。
- 在CTPA和NCCT数据上分别达到0.892和0.925的中心线Dice分数。
- 临床评估得分超4分（满分5），适合辅助肺病诊断使用。
准确分割肺部血管对多种肺部疾病的诊断与评估至关重要。当前多数自动化算法主要针对CTPA（增强型CT肺动脉造影）数据，但精度不足，且部分临床场景需支持NCCT（非增强型CT）数据。本研究提出一种用于从增强与非增强CT图像中自动分割肺部血管的3D图像分割算法。网络中设计了血管管腔结构优化模块（VLSOM），通过提取血管中心线（Cl）并基于位置信息调整权重，引入Cl-Dice Loss以监督血管结构稳定性。使用来自多厂商、多国家的427组高精度标注CT数据进行训练，结果显示：在CTPA数据上，中心线Dice（Cl-DICE）、中心线召回率（Cl-Recall）与召回率（Recall）分别为0.892、0.861、0.924；在NCCT数据上分别为0.925、0.903、0.949。模型在两种类型数据上均表现优异。进一步在独立外部测试集上开展临床视觉评估，平均得分分别为：准确性与鲁棒性4.26、分支丰富度4.17、诊断辅助价值4.33、血管连续性3.83（满分5分）。结果表明该方法具有显著临床应用潜力。
原文摘要 · Abstract (English)
Accurate segmentation of pulmonary vessels plays a very critical role in diagnosing and assessing various lung diseases. Currently, many automated algorithms are primarily targeted at CTPA (Computed Tomography Pulmonary Angiography) types of data. However, the segmentation precision of these methods is insufficient, and support for NCCT (Non-Contrast Computed Tomography) types of data is also a requirement in some clinical scenarios. In this study, we propose a 3D image segmentation algorithm for automated pulmonary vessel segmentation from both contrast-enhanced and non-contrast CT images. In the network, we designed a Vessel Lumen Structure Optimization Module (VLSOM), which extracts the centerline (Cl) of vessels and adjusts the weights based on the positional information and adds a Cl-Dice Loss to supervise the stability of the vessels structure. We used 427 sets of high-precision annotated CT data from multiple vendors and countries to train the model and achieved Cl-DICE, Cl-Recall, and Recall values of 0.892, 0.861, 0.924 for CTPA data and 0.925, 0.903, 0.949 for NCCT data. This shows that our model has achieved good performance in both accuracy and completeness of pulmonary vessel segmentation. We finally conducted a clinical visual assessment on an independent external test dataset. The average score for accuracy and robustness, branch abundance, assistance for diagnosis and vascular continuity are 4.26, 4.17, 4.33, 3.83 respectively while the full score is 5. These results highlight the great potential of this method in clinical application.
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