LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas

Sofija Engelson*,1Orcid, Jan Ehrhardt*,1,2, Timo Kepp2Orcid, Joshua Niemeijer3Orcid, Heinz Handels1,2Orcid
*: Equal contribution, 1: Institute of Medical Informatics, University of Lübeck, 2: German Research Center for Artificial Intelligence, 3: German Aerospace Center
Publication date: 2024/05/19
https://doi.org/10.59275/j.melba.2024-f95c
PDF · Code · arXiv

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.

Keywords

Mediastinal Lymph Node Segmentation · Anatomical Priors · Probabilistic Atlas · nnU-Net

Bibtex @article{melba:2024:009:engelson, title = "LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas", author = "Engelson, Sofija and Ehrhardt, Jan and Kepp, Timo and Niemeijer, Joshua and Handels, Heinz", journal = "Machine Learning for Biomedical Imaging", volume = "2", issue = "MICCAI 2023 LNQ challenge special issue", year = "2024", pages = "817--833", issn = "2766-905X", doi = "https://doi.org/10.59275/j.melba.2024-f95c", url = "https://melba-journal.org/2024:009" }
RISTY - JOUR AU - Engelson, Sofija AU - Ehrhardt, Jan AU - Kepp, Timo AU - Niemeijer, Joshua AU - Handels, Heinz PY - 2024 TI - LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas T2 - Machine Learning for Biomedical Imaging VL - 2 IS - MICCAI 2023 LNQ challenge special issue SP - 817 EP - 833 SN - 2766-905X DO - https://doi.org/10.59275/j.melba.2024-f95c UR - https://melba-journal.org/2024:009 ER -

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