Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
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DOI：
10.1088/0031-9155/52/12/010
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发表时间：
2007-06-21
影响因子：
3.5
通讯作者：
Visvikis, D.
中科院分区：
文献类型：
--
作者：
Hatt, M.;Lamare, F.;Visvikis, D.
准确的PET感兴趣体积(VOI)估计在不同的肿瘤学应用中是至关重要的，例如治疗反应评估和放射治疗计划。我们研究的目的是评估所提出的自动勾画病变体积的算法，即模糊隐马尔可夫链(FHMC)的性能，以及目前临床实践中基于阈值的技术的性能。与经典的隐马尔可夫链(HMC)算法一样，FHMC算法考虑了噪声、体素强度和空间相关性，从而将体素分类为背景或功能VOI。然而，模糊模型的新颖性在于包含了不精确度的估计，这应该随后导致在发射断层扫描数据中更好地模拟感兴趣对象边界的‘模糊’性质。算法的性能已经在IEC模型的模拟和采集数据集上进行了评估，涵盖了大范围的球形病变尺寸(从10 mm到37 mm)、对比度(4：1和8：1)和图像噪声水平。在使用两种不同体素大小(8 mm(3)和mm~3)的重建图像中评估病变活动恢复和VOI测定任务。为了同时考虑功能体积的位置和大小，在使用模拟数据集进行体积分割时，引入了分类误差百分比的概念。结果显示，考虑到对比度为4：1和病变大小为28 mm时，FHMC在确定功能体积或活动浓度恢复方面的表现明显好于基于阈值的方法。此外，对于FHMC算法提供的分段材积，所评估的分类误差和材积估计误差之间的差异较小。最后，与基于阈值的技术相比，自动算法的性能对图像噪声水平的影响较小。对于被评估的分割算法的性能而言，对模拟数据集和获取的数据集的分析导致了类似的结果和结论。
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes ( from 10 to 37 mm), contrast ratios (4: 1 and 8: 1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm(3) and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4: 1 and lesion sizes of < 28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.