Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
复制标题
使用模糊马尔可夫随机场模型对 PET/CT 图像进行自动肺肿瘤分割
DOI：
10.1155/2014/401201
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发表时间：
2014
影响因子：
--
通讯作者：
Wang P
中科院分区：
文献类型：
--
作者：
Guo Y;Feng Y;Sun J;Zhang N;Lin W;Sa Y;Wang P
正电子发射断层扫描(PET)和CT图像的结合为人体组织提供了互补的功能和解剖信息，并已被用于更好地确定肺癌的肿瘤体积。提出了一种稳健的基于PET/CT图像的肺肿瘤自动分割方法。该方法基于模糊马尔可夫随机场模型。在模糊马尔可夫随机场模型中，通过选取合适的观测特征的联合后验概率分布，实现了PET和CT图像信息的融合，比常用的高斯联合分布具有更好的性能。在这项研究中，使用7例非小细胞肺癌(NSCLC)患者的PET和CT模拟图像来评估该方法。分别使用所提出的方法和由经验丰富的放射肿瘤学家在融合图像上进行手动分割肿瘤。两种方法分割结果相似，Dice‘s相似性系数为0.85±0.013。结果表明，对于PET和CT图像中位于其他器官附近的肿瘤，如肿瘤延伸到胸壁或纵隔时，该方法可以实现有效的自动分割。
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
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