用动态梯度稀疏化训练，少标注数据下精准分割颈部淋巴结。
Dynamic Gradient Sparsification Training for Few-Shot Fine-tuning of CT Lymph Node Segmentation Foundation Model
- 基于动态梯度稀疏化，仅用少量标注数据高效微调基础模型。
- 在两大数据集上表现优于现有方法，36,106个淋巴结标注支持验证。
- 适合医学图像分割研究者，尤其关注少样本场景的临床应用。
准确的淋巴结（LN）分割对放疗治疗和预后分析至关重要，但受限于大规模标注数据的需求。尽管基于深度学习的分割基础模型在减少样本量的情况下展现高性能潜力，其在医疗领域的应用仍面临淋巴结领域先验缺失及复杂临床实践中少样本微调效率低的问题，凸显构建专用淋巴结分割基础模型的必要性。本工作从3,346个公开头颈部CT扫描中标注了36,106个可见淋巴结，建立了稳健的淋巴结分割模型（nnUNetv2）。在此基础上，提出动态梯度稀疏化训练（DGST），一种少样本微调方法，在保留基础知识的同时，动态更新最关键参数。我们在两个公开数据集SegRap2023和LNQ2023上验证该方法，结果表明DGST优于现有少样本微调方法，在有限标注数据下实现良好性能。我们开源了数据集、模型与全部代码：https://github.com/Zihaoluoh/LN-Seg-FM。
原文摘要 · Abstract (English)
Accurate lymph node (LN) segmentation is critical in radiotherapy treatment and prognosis analysis, but is limited by the need for large annotated datasets. While deep learning-based segmentation foundation models show potential in developing high-performing models with fewer samples, their medical adaptation faces LN domain-specific prior deficiencies and inefficient few-shot fine-tuning for complex clinical practices, highlighting the necessity of an LN segmentation foundation model. In this work, we annotated 36,106 visible LNs from 3,346 publicly available head-and-neck CT scans to establish a robust LN segmentation model (nnUNetv2). Building on this, we propose Dynamic Gradient Sparsification Training (DGST), a few-shot fine-tuning approach that preserves foundational knowledge while dynamically updating the most critical parameters of the LN segmentation model with few annotations. We validate it on two publicly available LN segmentation datasets: SegRap2023 and LNQ2023. The results show that DGST outperforms existing few-shot fine-tuning methods, achieving satisfactory performance with limited labeled data. We release the dataset, models and all implementations to facilitate relevant research: https://github.com/Zihaoluoh/LN-Seg-FM.
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