轮廓
掷骰子
公制(单位)
辐射敏感性
计算机科学
相似性(几何)
分割
断层治疗
人工智能
核医学
统计
放射治疗
数学
医学
放射科
计算机图形学(图像)
工程类
运营管理
图像(数学)
作者
Lucas McCullum,Kareem A. Wahid,Barbara Marquez,Clifton D. Fuller
出处
期刊:PubMed
[National Institutes of Health]
日期:2024-10-26
卷期号:2024: 755-758
被引量:2
标识
DOI:10.48550/arxiv.2410.20243
摘要
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.
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