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Model-based Segmentation of Pathological Lymph Nodes in CT Data

机译:CT数据中病理淋巴结的基于模型的分割

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For the computer-aided diagnosis of tumor diseases knowledge about the position, size and type of the lymph nodes is needed to compute the tumor classification (TNM). For the computer-aided planning of subsequent surgeries like the Neck Dissection spatial information about the lymph nodes is also important. Thus, an efficient and exact segmentation method for lymph nodes in CT data is necessary, especially pathological altered lymph nodes play an important role here.Based on prior work, in this paper we present a noticeably enhanced model-based segmentation method for lymph nodes in CT data, which now can be used also for enlarged and mostly well separated necrotic lymph nodes. Furthermore, the kind of pathological variation can be determined automatically during segmentation, which is important for the automatic TNM classification.Our technique was tested on 21 lymph nodes from 5 CT datasets, among several enlarged and necrotic ones. The results lie in the range of the inter-personal variance of human experts and improve the results of former work again. Bigger problems were only noticed for pathological lymph nodes with vague boundaries due to infiltrated neighbor tissue.
机译:对于肿瘤疾病的计算机辅助诊断,需要了解淋巴结的位置,淋巴结的尺寸和类型来计算肿瘤分类(TNM)。对于像颈部解剖空间信息的后续手术的计算机辅助规划,关于淋巴结的空间信息也很重要。因此,需要CT数据中的淋巴结的有效和精确的分段方法,特别是病理改变的淋巴结在这里起着重要作用。 基于现有工作,本文介绍了CT数据中的淋巴结基于基于模型的分段方法,现在也可以用于扩大和主要分离的坏死性淋巴结。此外,可以在分段期间自动确定病理变化的种类,这对于自动TNM分类很重要。 在几个扩大和坏死的中,我们的技术在5 CT数据集中的21个淋巴结测试。结果位于人类专家的间间差异范围,并再次改善前工作的结果。由于渗透邻邻组织而具有模糊边界的病理淋巴结才注意到更大的问题。

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