对比15种聚类算法，找出最适合动态全身PET图像分析的方法。
Quantitative evaluation of unsupervised clustering algorithms for dynamic total-body PET image analysis
- 用15种无监督聚类法分析动态PET的时序曲线。
- GMM、FCM和ICA+小批量K-means准确率超80%，每图处理快于半秒。
- 结果可直接用于临床疾病影像分析，适合医学影像研究者。
背景：由于新型扫描设备的出现，动态全身正电子发射断层成像（PET）已成为可能。尽管早期已有聚类算法用于PET分析，但针对动态全身PET图像的系统性评估仍较少。方法：本研究对比了15种无监督聚类方法，包括仅使用K-means、结合主成分分析（PCA）或独立成分分析（ICA）的K-means、高斯混合模型（GMM）、模糊C均值（FCM）、层次聚类、谱聚类及若干新聚类算法，用于分类动态PET图像中的时序活性曲线（TAC）。数据来自30名疑似或确诊冠心病患者的动态全身¹⁵O-水PET图像。通过将每幅图像中5000条TAC按来源（脑、右心室、右肾、右下肺叶、膀胱）进行分类，实现定量评估。结果：最佳方法为GMM、FCM和结合ICA的小批量K-means，其平均中位准确率分别为89%、83%和81%，每幅图像处理时间不超过半秒。结论：GMM、FCM及ICA与小批量K-means组合在动态全身PET分析中表现优异。
原文摘要 · Abstract (English)
Background. Recently, dynamic total-body positron emission tomography (PET) imaging has become possible due to new scanner devices. While clustering algorithms have been proposed for PET analysis already earlier, there is still little research systematically evaluating these algorithms for processing of dynamic total-body PET images. Materials and methods. Here, we compare the performance of 15 unsupervised clustering methods, including K-means either by itself or after principal component analysis (PCA) or independent component analysis (ICA), Gaussian mixture model (GMM), fuzzy c-means (FCM), agglomerative clustering, spectral clustering, and several newer clustering algorithms, for classifying time activity curves (TACs) in dynamic PET images. We use dynamic total-body $^{15}$O-water PET images collected from 30 patients with suspected or confirmed coronary artery disease. To evaluate the clustering algorithms in a quantitative way, we use them to classify 5000 TACs from each image based on whether the curve is taken from brain, right heart ventricle, right kidney, lower right lung lobe, or urinary bladder. Results. According to our results, the best methods are GMM, FCM, and ICA combined with mini batch K-means, which classified the TACs with a median accuracies of 89\%, 83\%, and 81\%, respectively, in a processing time of half a second or less on average for each image. Conclusion. GMM, FCM, and ICA with mini batch K-means show promise for dynamic total-body PET analysis.
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