mlsanityData to 7 Oct 2026

Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning

arXiv 2408.09411 ↗First posted 18 Aug 2024cs.CVcited by 1

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Abstract (arXiv)

Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potential metastatic pathways for cancer. For quantitative analysis, automatic segmentation of lymph nodes is crucial. However, due to the labor-intensive and time-consuming manual annotation process required for a large number of lymph nodes, it is more practical to annotate only a subset of the lymph node instances to reduce annotation costs. In this study, we propose a pre-trained Dual-Branch network with Dynamically Mixed Pseudo label (DBDMP) to learn from partial instance annotations for lymph nodes segmentation. To obtain reliable pseudo labels for lymph nodes that are not annotated, we employ a dual-decoder network to generate different outputs that are then dynamically mixed. We integrate the original weak partial annotations with the mixed pseudo labels to supervise the network. To further leverage the extensive amount of unannotated voxels, we apply a self-supervised pre-training strategy to enhance the model's feature extraction capability. Experiments on the mediastinal Lymph Node Quantification (LNQ) dataset demonstrate that our method, compared to directly learning from partial instance annotations, significantly improves the Dice Similarity Coefficient (DSC) from 11.04% to 54.10% and reduces the Average Symmetric Surface Distance (ASSD) from 20.83 mm to 8.72 mm. The code is available at https://github.com/WltyBY/LNQ2023trainingcode.git

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 41 references that are in the map. First posted on arXiv 18 Aug 2024.

Builds on

26 of 41 references.

References · 26

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14 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantificationcited by 6