A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET
A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET
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DOI：
10.1118/1.3301610
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发表时间：
2010-03-01
期刊：
影响因子：
3.8
通讯作者：
Zaidi, Habib
中科院分区：
文献类型：
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作者：
Belhassen, Saoussen;Zaidi, Habib
方法：为了克服这一局限性，提出了一种适用于典型的噪声和低分辨率肿瘤PET数据的模糊分割技术。采用非线性各向异性扩散滤波器对PET图像进行平滑处理，作为FCM算法的第二输入，以吸收空间信息（FCM- s）。此外，还开发了一种方法，将严重小波变换集成到标准FCM算法（FCM- sw）中，以允许处理异质病变的摄取。将该算法应用于NCAT幻影的模拟数据，结合21例组织学证实的非小细胞肺癌（NSCLC）患者和7例喉鳞癌（LSCC）患者的肺异质病变和临床PET/CT图像，评估其对任意大小、形状和示踪剂吸收的肿瘤的分割性能。对于NSCLC患者，手术标本宏观检查测得的最大肿瘤直径作为与分割技术估计的最大直径比较的基真值，而对于LSCC患者，三维宏观肿瘤体积作为与相应pet基体积比较的基真值。并将该算法与经典FCM分割技术进行了比较。结果：采用所提出的PET分割方法估计的原发性NSCLC肿瘤实际最大直径与宏观检查测量值具有良好的相关性（R-2=0.942），回归线与临床资料分组分析的同一性线（斜率=1.08）吻合良好。标准FCM算法似乎低估了临床数据的实际最大直径，导致所有数据集的平均误差为-4.6 mm（相对误差为-10.8 +/- 23.1%）。采用FCM-SW算法，最大直径估计的平均误差降至0.1 mm（0.9±14.4%）。同样，使用FCM- sw技术，LSCC患者估计体积的平均相对误差从FCM的21.7 +/- 22.0%降低到8.6 +/- 28.3%。结论：开发并评估了一种新的无监督PET图像分割技术，该技术允许在示踪剂摄取异质性存在的情况下对病变进行量化。这项技术正在进一步完善，并在临床环境中进行评估，以描绘pet引导放射治疗治疗计划的治疗量，但也可以在临床肿瘤学中找到其他应用，例如评估治疗反应。
Methods: To overcome this limitation, a new fuzzy segmentation technique adapted to typical noisy and low resolution oncological PET data is proposed. PET images smoothed using a nonlinear anisotropic diffusion filter are added as a second input to the proposed FCM algorithm to incorporate spatial information (FCM-S). In addition, a methodology was developed to integrate the agrave trous wavelet transform in the standard FCM algorithm (FCM-SW) to allow handling of heterogeneous lesions' uptake. The algorithm was applied to the simulated data of the NCAT phantom, incorporating heterogeneous lesions in the lung and clinical PET/CT images of 21 patients presenting with histologically proven nonsmall-cell lung cancer (NSCLC) and 7 patients presenting with laryngeal squamous cell carcinoma (LSCC) to assess its performance for segmenting tumors with arbitrary size, shape, and tracer uptake. For NSCLC patients, the maximal tumor diameters measured from the macroscopic examination of the surgical specimen served as the ground truth for comparison with the maximum diameter estimated by the segmentation technique, whereas for LSCC patients, the 3D macroscopic tumor volume was considered as the ground truth for comparison with the corresponding PET-based volume. The proposed algorithm was also compared to the classical FCM segmentation technique.Results: There is a good correlation (R-2=0.942) between the actual maximal diameter of primary NSCLC tumors estimated using the proposed PET segmentation procedure and those measured from the macroscopic examination, and the regression line agreed well with the line of identity (slope=1.08) for the group analysis of the clinical data. The standard FCM algorithm seems to underestimate actual maximal diameters of the clinical data, resulting in a mean error of -4.6 mm (relative error of -10.8 +/- 23.1%) for all data sets. The mean error of maximal diameter estimation was reduced to 0.1 mm (0.9 +/- 14.4%) using the proposed FCM-SW algorithm. Likewise, the mean relative error on the estimated volume for LSCC patients was reduced from 21.7 +/- 22.0% for FCM to 8.6 +/- 28.3% using the proposed FCM-SW technique.Conclusions: A novel unsupervised PET image segmentation technique that allows the quantification of lesions in the presence of heterogeneity of tracer uptake was developed and evaluated. The technique is being further refined and assessed in clinical setting to delineate treatment volumes for the purpose of PET-guided radiation therapy treatment planning but could find other applications in clinical oncology such as the assessment of response to treatment.