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Biological target volume delineation for a pediatric non-Hodgkin lymphoma: A semi-automated segmentation

机译:儿科非霍奇金淋巴瘤的生物靶体积描绘:半自动分割

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The current work is a case study with a segmentation and quantification of a lymphoma volume present in three dimensional (3D) images taken with positron emission tomography and fused with multilayer computerized tomography (PET-CT) belonging to a pediatric patient diagnosed with Non-Hodgkin's Lymphoma (NHL). A NHL tumor was manually delineated, and delineated with a semi-automated methodology. This last is divided in three stages: pre-processing, volumetric segmentation, and volume quantification. The pre-processing stage is based on the application of a filtering process aimed at increasing the quality of the information present in the 3D images. In order to achieve the image quality improvement a technique called Global Similarity Enhancement (GSE) is used, which is associated with the use of non-linear filtered algorithms. For the volumetric segmentation stage, a variational technique based on same level sets is used. The quantitative analysis of the segmented volumes allows for the delimitation of a NHL and the calculation of its BTV, but the semi-automated methodology must be calibrated with phantom simulated volumes, and compared with manual delineations, but that is left for subsequent studies.
机译:当前的工作是在与正电子发射断层拍摄,并与多层计算机化断层摄影术(PET-CT)稠合的三维(3D)图像的淋巴瘤体积存在的分割和量化属于诊断患有非霍奇金儿科患者的情况下,研究淋巴瘤(NHL)。甲NHL肿瘤手动圈定,并用半自动化方法描绘。预处理,体积分割,和体积定量:最后在三个阶段划分。前处理阶段是基于滤波处理旨在增加存在于3D图像的信息的质量的应用程序。为了实现图像质量改善了一个名为全局相似性增强技术(GSE)被使用,其是通过使用的非线性滤波算法相关联。对于体积分割阶段,使用基于相同的水平集的变分技术。所分割的体积的定量分析允许NHL的划界和其BTV的计算,但半自动化方法必须与幻象模拟量进行校准,并设有手动delineations相比,但其留给随后的研究。

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