Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy.
Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy.
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DOI：
10.1016/j.phro.2019.12.001
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发表时间：
2020-01
影响因子：
--
通讯作者：
van Elmpt W
中科院分区：
文献类型：
--
作者：
Vaassen F;Hazelaar C;Vaniqui A;Gooding M;van der Heyden B;Canters R;van Elmpt W
自动划定软件在节省时间方面显示了良好的效果。标准几何测量与圈定时间的相关性不高。引入了新的评价方法：附加路径长度(APL)和表面DSC。(补充)路径长度与时间记录的相关性最高。这使得APL成为临床有效性的最具代表性的衡量标准。在放射治疗中，自动危险器官分割算法可以更快地描绘出轮廓，但临床相关的轮廓评估仍然具有挑战性。评估自动轮廓的常用方法，如体积骰子相似系数(DSC)或Hausdorff距离，已被证明是几何相似性的良好度量，但并不总是与轮廓的临床适用性或调整它们所需的时间相关。这项研究旨在评估新的和常用的评估方法与轮廓绘制过程中节省时间的相关性。20名肺癌患者被用来比较基于图谱的轮廓绘制和深度学习轮廓绘制与手动轮廓绘制后的用户调整。记录调整自动轮廓与手动轮廓相比所需的绝对时间(S)，由此计算出相对节省的时间(%)。新的评价指标(表面DSC和附加路径长度APL)和传统的评价指标(体积DSC和Hausdorff距离)与时间记录和时间节省相关，并用皮尔逊相关系数R进行量化，其中APL与绝对适应时间的相关性最高(R0=0.87)。表面−与绝对适应时间(R=−0.6 9)和相对节省时间(R=0.5 7)的相关性较低。容量差示扫描量热法和豪斯道夫距离与绝对适应时间(R0=0.32和−0.64)和相对节省时间(R0=0.44和−0.64)的相关系数较低。表面DSC和APL是使用自动分割时轮廓自适应时间和节省时间的更好的指标，与常用的基于几何的测量相比，它们为自动生成的轮廓质量提供了更多的临床相关性和更好的定量测量。
Automatic delineation software shows promising results in terms of time-saving. Standard geometry measures do not have a high correlation with delineation time. New evaluation measures were introduced: added path length (APL) and surface DSC. (Added) path length showed the highest correlation with time-recordings. This makes APL the most representative measure for clinical usefulness. In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring. Twenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R. The highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = −0.38), for surface DSC with absolute adaption time (R = −0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = −0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and −0.64, respectively). Surface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures.
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DOI：
10.1016/j.ijrobp.2009.09.062
发表时间：
2010-07-01
影响因子：
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影响因子：
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