梯度图辅助的头颈部肿瘤分割：MRI引导放疗中从前中期到中期的方案
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy
AI总结：
本研究提出利用前中期图像和梯度图作为先验知识，增强MRI引导自适应放疗中的头颈癌中期肿瘤分割，在挑战赛测试集上GTVp和GTVn的DSCagg平均得分达0.70，有助于改进治疗规划。
摘要：
放射治疗（RT）是头颈癌治疗的重要组成部分，其中对大体肿瘤体积（GTV）的准确分割对有效治疗规划至关重要。本研究探讨了在MRI引导自适应放疗中，利用前中期（pre-RT）肿瘤区域和局部梯度图来增强头颈癌中期（mid-RT）肿瘤分割的方法。通过将前中期图像及其分割结果作为先验知识，我们解决了中期分割中的肿瘤定位难题。我们计算了前中期图像中肿瘤区域的梯度图，并将其应用于中期图像以改善肿瘤边界描绘。我们的方法在原发GTV（GTVp）和淋巴结GTV（GTVn）的分割精度上均有提升，尽管性能受到数据限制的影响。在挑战赛测试集评估中，最终的DSCagg得分分别为：GTVp 0.534，GTVn 0.867，平均得分0.70。该方法显示出在自适应放疗中提升分割与治疗规划的潜力。团队：DCPT-Stine's group。
英文摘要：
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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