Dornheim Jana, Seim Heiko, Preim Bernhard, Hertel Ilka, Strauss Gero
Otto-von-Guericke-Universität Magdeburg, Germany.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):904-11. doi: 10.1007/11866763_111.
The quantitative assessment of neck lymph nodes in the context of malign tumors requires an efficient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D Mass-Spring Model for lymph node segmentation in CT datasets. Our model for the first time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. Our model design and segmentation accuracy are both evaluated with lymph nodes from clinical CT neck datasets.
在恶性肿瘤背景下对颈部淋巴结进行定量评估，需要一种有效的断层3D数据集中淋巴结分割技术。我们提出了一种用于CT数据集中淋巴结分割的稳定3D质量弹簧模型。我们的模型首次同时表示了特征灰度值范围、定向轮廓信息以及形状知识，这导致了一个更加稳健和高效的分割过程。我们通过临床颈部CT数据集中的淋巴结对模型设计和分割精度进行了评估。
