OAR-Weighted Dice Score: A spatially aware, radiosensitivity aware metric for target structure contour quality assessment.

Lucas McCullum, Kareem A Wahid, Barbara Marquez, Clifton D Fuller
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Abstract

The Dice Similarity Coefficient (DSC) is the current de facto standard to determine agreement between a reference segmentation and one generated by manual / auto-contouring approaches. This metric is useful for non-spatially important images; however, radiation therapy requires consideration of nearby Organs-at-Risk (OARs) and their radiosensitivity which are currently unaccounted for with the traditional DSC. In this work, we introduce the OAR-DSC which accounts for nearby OARs and their radiosensitivity when computing the DSC. We illustrate the importance of this through cases where two proposed contours have similar DSC, but lower OAR-DSC when one contour expands closer to the surrounding OARs. This work is important because the OAR-DSC may be used by deep learning auto-contouring algorithms in a radiation therapy specific loss function, thereby progressing on the current disregard for the importance of these differences on the final radiation dose plan generation, delivery, and risks of patient toxicity.

OAR 加权骰子得分:用于目标结构轮廓质量评估的空间感知、辐射敏感度感知度量。
骰子相似系数(DSC)是目前确定参考分割与手动/自动轮廓方法生成的分割之间一致性的事实标准。这一指标对非空间重要图像很有用,但放射治疗需要考虑附近的风险器官(OAR)及其辐射敏感性,而传统的 DSC 目前还没有考虑到这些因素。在这项工作中,我们引入了 OAR-DSC,在计算 DSC 时考虑附近的 OAR 及其辐射敏感性。我们通过以下案例说明了这一点的重要性:两个拟议的等值线具有相似的 DSC,但当其中一个等值线扩展到更靠近周围的 OAR 时,OAR-DSC 就会降低。这项工作非常重要,因为深度学习自动轮廓算法可能会将 OAR-DSC 用于放射治疗特定损失函数中,从而改善目前无视这些差异对最终放射剂量计划的生成、传输和患者毒性风险的重要性的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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