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Automatic Identification of IASLC-defined Mediastinal Lymph Node Stations on CT scans using Multi-atlas Organ Segmentation

机译:使用多图谱器官分割在CT扫描上自动识别IASLC定义的纵隔淋巴结站

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Station-labeling of mediastinal lymph nodes is typically performed to identify the location of enlarged nodes for cancer staging. Stations are usually assigned in clinical radiology practice manually by qualitative visual assessment on CT scans, which is time consuming and highly variable. In this paper, we developed a method that automatically recognizes the lymph node stations in thoracic CT scans based on the anatomical organs in the mediastinum. First, the trachea, lungs, and spines are automatically segmented to locate the mediastinum region. Then, eight more anatomical organs are simultaneously identified by multi-atlas segmentation. Finally, with the segmentation of those anatomical organs, we convert the text definitions of the International Association for the Study of Lung Cancer (IASLC) lymph node map into patient-specific color-coded CT image maps. Thus, a lymph node station is automatically assigned to each lymph node. We applied this system to CT scans of 86 patients with 336 mediastinal lymph nodes measuring equal or greater than 10 mm. 84.8% of mediastinal lymph nodes were correctly mapped to their stations.
机译:通常进行纵隔淋巴结的驻地标记以确定癌症分期的扩大节点的位置。通过对CT扫描的定性视觉评估,手动在临床放射学实践中分配站,这是耗时和高度可变的。在本文中,我们开发了一种基于纵隔的解剖器官自动识别胸腔CT扫描中的淋巴结站的方法。首先,气管,肺和脊柱被自动分段以定位纵隔区域。然后,通过多标准分割同时识别出八个更多的解剖器。最后,随着这些解剖器官的分割,我们将国际肺癌(IASLC)淋巴结地图研究的文本定义转换为患者特异性颜色编码CT图像映射。因此,将淋巴结站自动分配给每个淋巴结。我们将该系统应用于86名患者的CT扫描,336例纵隔淋巴结测量等于或大于10毫米。将84.8%的纵隔淋巴结正确地映射到其站。

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