A Lung Cancer Auxiliary Diagnostic Method: Deep Learning Based Mediastinal Lymphatic Partitions Segmentation for Cancer Staging.

Abstract

PURPOSE/OBJECTIVE(S)\nAccurate assessment of lymph node involvement plays an essential role in lung cancer treatment. The lymph node zonation map is widely used to depict the location of lymph node metastases, which could effectively reflect the degree of tumor invasion. According to the IASLC partitioning criteria and clinical work experience, a total of 15 partitions should be closely monitored. To facilitate doctors in their diagnostic work and improve their efficiency, we developed an automatic mediastinal lymph partitions segmentation algorithm in this study to provide direct visual guidance of the mediastinal area. However, the complex anatomical structure, ambiguous borders, similarity between targets, and variation between cases make the segmentation task more difficult.\n\n\nMATERIALS/METHODS\nIn this study, we propose a novel two-stage deep learning model for the automatic segmentation of all 15 mediastinal lymph partitions. U-Net is used as the backbone to extract spatial features to establish the mapping from original CT images to the segmentation mask in the first stage. Residual and dense connections are combined in the networks to construct to enhance the propagation of features and characterization effect of the models. An attention mechanism is designed which uses convolutional LSTM layers to control the attention in the feature transmission process to enable the model to focus more on the target area. To tackle the hard samples, which could not be segmented correctly only based on spatial features, we designed a convolutional recurrent network with LSTM structure to fuse features of different scales in the second stage to exploit temporal features to produce more granular segmentation results.\n\n\nRESULTS\nA total of 80 CT data cases with different standards in some aspects are collected in this study and annotated by two experts based on the IASLC criteria. For the quantitative evaluation of our proposed method, an average Dice coefficient of 0.78 is achieved of fifteen partitions on the independent test set, which has exceeded the performance of widely used segmentation approaches and reached an advanced level. To further evaluate our method s approbation degree, a user study is conducted, which demonstrates the effectiveness of the designed method with a score of 4 out of 5.\n\n\nCONCLUSION\nThis study is the first to design and implement the automatic segmentation algorithm for mediastinal lymphatic partitions, facilitating doctors work in diagnosis and treatment planning compared with manual identification. Qualitative and quantitative evaluations indicate the effectiveness and advancements of the algorithm.