用半监督深度学习精准估算增强CT中胸腔积液体积
AI-Assisted Pleural Effusion Volume Estimation from Contrast-Enhanced CT Images
- 提出教师-助教-学生框架，仅需少量标注数据即可训练
- 分割准确率提升，体积误差降低至23.16ml的四分之一
- 适合临床医生快速评估积液量，提升诊疗效率
背景：胸腔积液是多种临床状况的常见表现，但通过CT扫描精确测量其体积具有挑战性。目的：为改善积液分割与量化，以支持临床管理，本研究在增强CT图像上开发并训练了一种半监督深度学习框架。材料与方法：回顾性收集内部及外部数据集中的CT肺动脉造影（CTPA）数据，其中100例进行手动标注用于模型训练，其余用于测试与验证。提出一种新型半监督深度学习框架——教师-助教-学生（TTAS），可在未分割的检查中实现高效训练。分割性能与现有先进模型对比。结果：共纳入100名患者（平均年龄72岁，标准差28；55名为男性）。与当前最优模型nnU-Net相比，TTAS模型表现出更优的分割性能，平均Dice分数达0.82（95%置信区间：0.79–0.84），显著高于nnU-Net的0.73（p < 0.0001，Student's T检验）。此外，TTAS的平均绝对体积差异（AbVD）仅为6.49 mL（95%置信区间：4.80–8.20），较nnU-Net的23.16 mL降低四倍（p < 0.0001）。结论：所提出的TTAS框架在胸腔积液体积分割中表现更优，有助于从CT扫描中实现精准体积判定。
原文摘要 · Abstract (English)
Background: Pleural Effusions (PE) is a common finding in many different clinical conditions, but accurately measuring their volume from CT scans is challenging. Purpose: To improve PE segmentation and quantification for enhanced clinical management, we have developed and trained a semi-supervised deep learning framework on contrast-enhanced CT volumes. Materials and Methods: This retrospective study collected CT Pulmonary Angiogram (CTPA) data from internal and external datasets. A subset of 100 cases was manually annotated for model training, while the remaining cases were used for testing and validation. A novel semi-supervised deep learning framework, Teacher-Teaching Assistant-Student (TTAS), was developed and used to enable efficient training in non-segmented examinations. Segmentation performance was compared to that of state-of-the-art models. Results: 100 patients (mean age, 72 years, 28 [standard deviation]; 55 men) were included in the study. The TTAS model demonstrated superior segmentation performance compared to state-of-the-art models, achieving a mean Dice score of 0.82 (95% CI, 0.79 - 0.84) versus 0.73 for nnU-Net (p < 0.0001, Student's T test). Additionally, TTAS exhibited a four-fold lower mean Absolute Volume Difference (AbVD) of 6.49 mL (95% CI, 4.80 - 8.20) compared to nnU-Net's AbVD of 23.16 mL (p < 0.0001). Conclusion: The developed TTAS framework offered superior PE segmentation, aiding accurate volume determination from CT scans.
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