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Snake model based lymphoma segmentation for sequential CT images

机译：用于连续CT图像的基于Snake模型的淋巴瘤分割

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摘要

The measurement of the size of lesions in follow-up CT examinations of cancer patients is important to evaluate the success of treatment. This paper presents an automatic algorithm for identifying and segmenting lymph nodes in CT images across longitudinal time points. Firstly, a two-step image registration method is proposed to locate the lymph nodes including coarse registration based on body region detection and fine registration based on a double-template matching algorithm. Then, to make the initial segmentation approximate the boundaries of lymph nodes, the initial image registration result is refined with intensity and edge information. Finally, a snake model is used to evolve the refined initial curve and obtain segmentation results. Our algorithm was tested on 26 lymph nodes at multiple time points from 14 patients. The image at the earlier time point was used as the baseline image to be used in evaluating the follow-up image, resulting in 76 total test cases. Of the 76 test cases, we made a 76 (100%) successful detection and 38/40 (95%) correct clinical assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). The quantitative evaluation based on several metrics, such as average Hausdorff distance, indicates that our algorithm is produces good results. In addition, the proposed algorithm is fast with an average computing time 2.58s. The proposed segmentation algorithm for lymph nodes is fast and can achieve high segmentation accuracy, which may be useful to automate the tracking and evaluation of cancer therapy.
机译：在癌症患者的后续CT检查中测量病变的大小对于评估治疗的成功性很重要。本文提出了一种自动算法，用于识别和分割纵向时间点上CT图像中的淋巴结。首先，提出了一种基于两步图像配准的图像淋巴结定位方法：基于人体区域检测的粗略配准和基于双模板匹配算法的精细配准。然后，为了使初始分割近似于淋巴结的边界，利用强度和边缘信息对初始图像配准结果进行细化。最后，使用蛇模型来发展精炼的初始曲线并获得分割结果。我们的算法在14个患者的多个时间点的26个淋巴结上进行了测试。较早时间点的图像用作评估后续图像的基准图像，总共有76个测试案例。根据实体瘤反应评估标准（RECIST），在76个测试案例中，我们成功进行了76（100％）次检测，并进行了38/40（95％）正确的临床评估。基于几个指标（例如平均Hausdorff距离）的定量评估表明，我们的算法取得了良好的效果。另外，该算法速度快，平均计算时间为2.58s。提出的淋巴结分割算法速度快，分割精度高，可能有助于癌症治疗的跟踪和评估。

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