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Tumor-node-metastasis (TNM) classification for lung cancer is essential for appropriate treatment strategies and has been used widely in the investigation and treatment of this cancer. In TNM classification, N descriptors are one of the most important prognostic indicators and are determined by the metastatic lymph node stations. Therefore, accurate classification of lymph nodes is crucial. Thoracic contrast-enhanced Computed Tomography (CT) images represent the gold-standard modality. However, manual segmentation and classification of lymph nodes are challenges that arise from the relatively similar attenuation between lymph nodes and surrounding structures. Recent progress of convolutional neural network (CNN) has spawned research on mediastinal lymph nodes segmentation on chest CT images using CNNs. However, the previous CNN-based method did not consider the relationship between airways and lymph node locations for segmenting the thoracic N1 lymph nodes group. In this study, we investigate whether distance maps based on tracheobronchial labeling can represent the anatomy properties of the N1 lymph nodes group in volumetric CT images using the NIH open-source dataset.
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