头颈部肿瘤PET图像分割随机游走方法

针对肿瘤放疗生物靶区高精度勾画难题，根据头颈部肿瘤PET(positron emission computed tomography)影像特点，提出了肿瘤PET图像分割随机游走方法.首先，根据PET SUV(standardized uptake value)影像，采用三维自适应区域生长和数学形态学膨胀方法确定随机游走方法的种子点，将包含肿瘤的感兴趣区域分为核心肿瘤区域(标记为前景种子点)、正常组织区域(标记为背景种子点)和待定区域.然后，利用头颈部肿瘤和周围正常组织PET图像具有不同的对比度纹理特征，将PET SUV及其对比度纹理值作为随机游走方法中边的权值计算依据.实验结果表明，该法不仅比传统随机游走方法平均提速9.34倍，而且，以临床医生手工勾画的大体肿瘤区作为参考标准，相似度平均提高32.5%(P<0.05).本文方法能够有效地自动勾画头颈部肿瘤放疗生物靶区.
In order to solve the problem of the high accuracy delineation of biological target volume (BTV) for the radiotherapy of head and neck cancer, a random walk method was proposed by using PET (positron emission computed tomography) image features of tumors.Firstly, the selected region of interest (ROI) was segmented into the primary tumor (labeled as foreground seeds), normal tissue (labeled as background seeds) and pending region by three-dimensional adaptive region growing and morphological dilation based on PET SUV images.Secondly, due to the differences of contrast texture feature of head and neck tumor and surrounding normal tissues in PET images, the contrast texture feature was incorporated into the weights of random walk(RW) to further improve the accuracy of tumor segmentation results.Clinical PET image segmentations of head and neck cancer have shown that the improved RW is 9.34 times faster than the traditional RW on average.And the similarity is increased by 32.5% on average if the gross tumor volume delineated by clinicians is considered as the ground truth (P<0.05).The proposed method is an efficient and accurate method for the delineation of the BTV corresponding to head and neck tumors.