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LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification.
MELBA 3, 1 - 15 (2025)
Accurate assessment of lymph node size in 3D CT scans is crucial for
cancer staging, therapeutic management, and monitoring treatment
response. Existing state-of-the-art segmentation frameworks in medical
imaging often rely on fully annotated datasets. However, for lymph node
segmentation, these datasets are typically small due to the extensive
time and expertise required to annotate the numerous lymph nodes in 3D
CT scans. Weakly-supervised learning, which leverages incomplete or
noisy annotations, has recently gained interest in the medical imaging
community as a potential solution. Despite the variety of
weakly-supervised techniques proposed, most have been validated only on
private datasets or small publicly available datasets. To address this
limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge
was organized in conjunction with the 26th International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI
2023). This challenge aimed to advance weakly-supervised segmentation
methods by providing a new, partially annotated dataset and a robust
evaluation framework. A total of 16 teams from 5 countries submitted
predictions to the validation leaderboard, and 6 teams from 3 countries
participated in the evaluation phase. The results highlighted both the
potential and the current limitations of weakly-supervised approaches.
On one hand, weakly-supervised approaches obtained relatively good
performance with a median Dice score of 61.0%. On the other hand,
top-ranked teams, with a median Dice score exceeding 70%, boosted their
performance by leveraging smaller but fully annotated datasets to
combine weak supervision and full supervision. This highlights both the
promise of weakly-supervised methods and the ongoing need for
high-quality, fully annotated data to achieve higher segmentation
performance.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Meeting abstract
Schlagwörter
Benchmark (surveying) ; Mediastinal Lymph Node
ISSN (print) / ISBN
2766-905X
e-ISSN
2766-905X
Quellenangaben
Band: 3,
Heft: MICCAI 2023 LNQ challenge,
Seiten: 1 - 15
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)