Publication
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas
Sofija Engelson; Jan Ehrhardt; Timo Kepp; Joshua Niemeijer; Heinz Handels
In: ArXiv e-prints (arxiv), Vol. arXiv:2406.03984, Pages 1-17, arXiv, 6/2024.
Abstract
The evaluation of lymph node metastases plays a crucial role in achieving precise cancer
staging, which in turn influences subsequent decisions regarding treatment options. The
detection of lymph nodes 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 automatic 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.