用治疗前影像辅助治疗中肿瘤分割，提升头颈癌放疗精准度。
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy
- 利用治疗前影像的分割结果和梯度图作为先验知识
- 治疗中肿瘤边界分割准确率提升，节点瘤平均DSC达0.867
- 适合需要自适应放疗的临床医生和医学影像算法研究者
放疗是头颈癌治疗的关键手段，精确分割大体肿瘤体积（GTV）对有效治疗规划至关重要。本研究探索在磁共振引导的自适应放疗中，利用治疗前肿瘤区域及局部梯度图来提升治疗中肿瘤分割的准确性。通过将治疗前图像及其分割结果作为先验知识，解决治疗中肿瘤定位难题。计算治疗前图像中肿瘤区域的梯度图，并应用于治疗中图像以增强边界识别。该方法在主要肿瘤（GTVp）和淋巴结肿瘤（GTVn）分割上均表现更优，受限于数据量，性能未完全发挥。挑战测试集最终平均总分DSCagg为0.534（GTVp）、0.867（GTVn），均值0.70。该方法在自适应放疗中具有提升分割与治疗规划的潜力。
原文摘要 · Abstract (English)
Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions and local gradient maps to enhance mid-RT tumor segmentation for head and neck cancer in MRI-guided adaptive radiotherapy. By leveraging pre-RT images and their segmentations as prior knowledge, we address the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed and applied to mid-RT images to improve tumor boundary delineation. Our approach demonstrated improved segmentation accuracy for both primary GTV (GTVp) and nodal GTV (GTVn), though performance was limited by data constraints. The final DSCagg scores from the challenge's test set evaluation were 0.534 for GTVp, 0.867 for GTVn, and a mean score of 0.70. This method shows potential for enhancing segmentation and treatment planning in adaptive radiotherapy. Team: DCPT-Stine's group.
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