Comparison of anatomical priors for learning-based neural network guidance for mediastinal lymph node segmentation

The assessment of lymph node metastases is critical for accurate cancer staging and consequently the decision for treatment options. Lymph node staging is a challenging, time-consuming task due to the fact that lymph nodes have ill-defined borders as well as varying sizes and morphological characteristics. The purpose of this study is to evaluate the effects of using different anatomical priors with the aim of guiding network attention within the application of segmentation of pathological lymph nodes in the mediastinum. The first presented prior, a distance map, displays the distance to a commonly defined point across all patients and, thus, provides an orientation of where a patch is extracted from. The second prior option, a probabilistic lymph node atlas, provides a map of areas where healthy and pathological lymph nodes are located, but also highlights lymph node stations that are more likely to become malignant. The distance map as well as the probabilistic lymph node atlas are results of an upstream atlas-to-patient registration approach. The third prior is a combination of segmentation masks of anatomical structures generated by the TotalSegmentator algorithm. A paired t-test on 5-fold cross validated results shows no significant differences in Dice score between models trained with the distance map or/and the probabilistic lymph node atlas compared to models trained with CT only. Counterintuitively, the models trained with segmentation masks of selected anatomical structures show significantly decreased segmentation accuracy. However, using the probabilistic lymph node atlas reduces the number of false negatives and consistently elevates the effect of post-processing.