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

2026-04-11

AI Summary

This study addresses the challenges of time-consuming manual delineation and frequent omission of small-volume metastatic lymph nodes in head and neck cancer radiotherapy. To overcome these limitations, the authors propose a volume-aware Dice loss function integrated with selective and dual-mask strategies within an nnU-Net ResEnc M architecture for multi-label automatic segmentation of primary tumors and metastatic lymph nodes. By incorporating volume-sensitive weighting, the method enhances detection sensitivity for small lesions. Evaluated on the HNTS-MRG 2024 dataset, the dual-mask approach achieves a lymph node detection sensitivity of 83.46% while maintaining a primary tumor segmentation Dice score of 82.04%. In contrast, the selective strategy improves the lymph node Dice coefficient to 0.758 but at the cost of reduced primary tumor segmentation accuracy.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• target volume auto-delineation
• head and neck cancer
• metastatic lymph nodes
• adaptive MR-guided radiotherapy
• inter-observer variability

Innovation

• Volume-Aware Dice Loss
• Auto-segmentation
• Adaptive MR-guided Radiotherapy
• Head and Neck Cancer
• nnU-Net