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Full three-dimensional segmentation and quantification of tumor vessels for photoacoustic images
Photoacoustics ( IF 7.1 ) Pub Date : 2020-10-07 , DOI: 10.1016/j.pacs.2020.100212
Mingjian Sun , Chao Li , Ningbo Chen , Huangxuan Zhao , Liyong Ma , Chengbo Liu , Yi Shen , Riqiang Lin , Xiaojing Gong

Quantitative analysis of tumor vessels is of great significance for tumor staging and diagnosis. Photoacoustic imaging (PAI) has been proven to be an effective way to visualize comprehensive tumor vascular networks in three-dimensional (3D) volume, while previous studies only quantified the vessels projected in one plane. In this study, tumor vessels were segmented and quantified in a full 3D framework. It had been verified in the phantom experiments that the 3D quantification results have better accuracy than 2D. Furthermore, in vivo vessel images were quantified by 2D and 3D quantification methods respectively. And the difference between these two results is significant. In this study, complete vessel segmentation and quantification method within a 3D framework was implemented, which showed obvious advantage in the analysis accuracy of 3D photoacoustic images, and potentially improve tumor study and diagnosis.



中文翻译:

用于光声图像的肿瘤血管的完整三维分割和量化

肿瘤血管的定量分析对肿瘤的分期和诊断具有重要意义。光声成像(PAI)已被证明是在三维(3D)体积中可视化完整肿瘤血管网络的有效方法,而先前的研究仅对投射在一个平面上的血管进行了量化。在这项研究中,在完整的3D框架中对肿瘤血管进行了分割和量化。在体模实验中已经证明3D定量结果比2D具有更好的准确性。此外,分别通过2D和3D定量方法对体内血管图像进行定量。并且这两个结果之间的差异是显着的。在这项研究中,在3D框架内实施了完整的血管分割和量化方法,

更新日期:2020-10-16
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