  The evaluation of lymph node metastases plays a crucial role in achieving
precise cancer staging, influencing subsequent decisions regarding treatment
options. Lymph node detection poses challenges due to the presence of unclear
boundaries and the diverse range of sizes and morphological characteristics,
making it a resource-intensive process. As part of the LNQ 2023 MICCAI
challenge, we propose the use of anatomical priors as a tool to address the
challenges that persist in mediastinal lymph node segmentation in combination
with the partial annotation of the challenge training data. The model ensemble
using all suggested modifications yields a Dice score of 0.6033 and segments
57% of the ground truth lymph nodes, compared to 27% when training on CT only.
Segmentation accuracy is improved significantly by incorporating a
probabilistic lymph node atlas in loss weighting and post-processing. The
largest performance gains are achieved by oversampling fully annotated data to
account for the partial annotation of the challenge training data, as well as
adding additional data augmentation to address the high heterogeneity of the CT
images and lymph node appearance. Our code is available at
https://github.com/MICAI-IMI-UzL/LNQ2023.
