改进头颈癌放疗中肿瘤自动勾画，提升小淋巴结检测精度。
Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss
- 用体积感知的损失函数优化深度学习模型，增强对小淋巴结的识别。
- 选择性加权使淋巴结检测灵敏度达84.93%，但主肿瘤精度下降至63.65%。
- 双掩码策略平衡大小病灶表现，适合多目标分割任务。
头颈癌（HNC）靶区手动勾画存在观察者差异大、耗时长的问题。本研究将体积感知（VA）Dice损失函数集成到自配置深度学习框架中，用于自适应MR引导放疗中的原发肿瘤（PT）和转移性淋巴结（LN）自动分割。基于HNTS-MRG 2024数据集，采用nnU-Net ResEnc M架构，对比标准Dice损失与两种VA配置：双掩码（对PT和LN均使用VA损失）和选择性淋巴结掩码（仅对LN使用VA损失）。评估指标包括体积Dice分数、表面距离指标（SDS、MSD、HD95）及病灶级二分类检测敏感性和精确率。结果显示，选择性淋巴结掩码配置在淋巴结体积Dice分数上最优（0.758 vs. 0.734），且淋巴结病灶检测灵敏度显著提升（84.93% vs. 81.80%），但原发肿瘤检测精确率大幅下降（63.65% vs. 81.27%）。双掩码配置则在两者间取得更好平衡，保持原发肿瘤精确率82.04%，同时将淋巴结灵敏度提升至83.46%。结论表明，体积敏感损失函数有效缓解了小转移淋巴结的漏检问题；尽管选择性加权可优化淋巴结检测，但在多目标分割中仍需双掩码策略以保证大体积原发肿瘤的分割准确性。
原文摘要 · Abstract (English)
Background: Manual delineation of target volumes in head and neck cancer (HNC) remains a significant bottleneck in radiotherapy planning, characterized by high inter-observer variability and time consumption. This study evaluates the integration of a Volume-Aware (VA) Dice loss function into a self-configuring deep learning framework to enhance the auto-segmentation of primary tumors (PT) and metastatic lymph nodes (LN) for adaptive MR-guided radiotherapy. We investigate how volume-sensitive weighting affects the detection of small, anatomically complex nodal metastases compared to conventional loss functions. Methods: Utilizing the HNTS-MRG 2024 dataset, we implemented an nnU-Net ResEnc M architecture. We conducted a multi-label segmentation task, comparing a standard Dice loss baseline against two Volume-Aware configurations: a "Dual Mask" setup (VA loss on both PT and LN) and a "Selective LN Mask" setup (VA loss on LN only). Evaluation metrics included volumetric Dice scores, surface-based metrics (SDS, MSD, HD95), and lesion-wise binary detection sensitivity and precision. Results: The Selective LN Mask configuration achieved the highest LN Volumetric Dice Score (0.758 vs. 0.734 baseline) and significantly improved LN Lesion-Wise Detection Sensitivity (84.93% vs. 81.80%). However, a critical trade-off was observed; PT detection precision declined significantly in the selective setup (63.65% vs. 81.27%). The Dual Mask configuration provided the most balanced performance across both targets, maintaining primary tumor precision at 82.04% while improving LN sensitivity to 83.46%. Conclusions: A volume-sensitive loss function mitigated the under-representation of small metastatic lesions in HNC. While selective weighting yielded the best nodal detection, a dual-mask approach is required in multi-label tasks to maintain segmentation accuracy for larger primary tumor volumes.
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