Pathological lymph node delineation is crucial in cancer diagnosis,
progression assessment, and treatment planning. The MICCAI 2023 Lymph Node
Quantification Challenge published the first public dataset for pathological
lymph node segmentation in the mediastinum. As lymph node annotations are
expensive, the challenge was formed as a weakly supervised learning task, where
only a subset of all lymph nodes in the training set have been annotated. For
the challenge submission, multiple methods for training on these weakly
supervised data were explored, including noisy label training, loss masking of
unlabeled data, and an approach that integrated the TotalSegmentator toolbox as
a form of pseudo labeling in order to reduce the number of unknown voxels.
Furthermore, multiple public TCIA datasets were incorporated into the training
to improve the performance of the deep learning model. Our submitted model
achieved a Dice score of 0.628 and an average symmetric surface distance of
5.8~mm on the challenge test set. With our submitted model, we accomplished
third rank in the MICCAI2023 LNQ challenge. A finding of our analysis was that
the integration of all visible, including non-pathological, lymph nodes
improved the overall segmentation performance on pathological lymph nodes of
the test set. Furthermore, segmentation models trained only on clinically
enlarged lymph nodes, as given in the challenge scenario, could not generalize
to smaller pathological lymph nodes. The code and model for the challenge
submission are available at
https://gitlab.lrz.de/compai/MediastinalLymphNodeSegmentation.

研究中以病理性淋巴结划分为关键，探讨了 MICCAI 2023 淋巴结定量挑战赛中弱监督学习、深度学习模型以及分割性能的相关内容。