Rodríguez Outeiral Roque, Ferreira Silvério Nicole, González Patrick J, Schaake Eva E, Janssen Tomas, van der Heide Uulke A, Simões Rita
Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
Phys Imaging Radiat Oncol. 2023 Oct 13;28:100500. doi: 10.1016/j.phro.2023.100500. eCollection 2023 Oct.
Existing methods for quality assurance of the radiotherapy auto-segmentations focus on the correlation between the average model entropy and the Dice Similarity Coefficient (DSC) only. We identified a metric directly derived from the output of the network and correlated it with clinically relevant metrics for contour accuracy.
Magnetic Resonance Imaging auto-segmentations were available for the gross tumor volume for cervical cancer brachytherapy (106 segmentations) and for the clinical target volume for rectal cancer external-beam radiotherapy (77 segmentations). The nnU-Net's output before binarization was taken as a score map. We defined a metric as the mean of the voxels in the score map above a threshold (λ). Comparisons were made with the mean and standard deviation over the score map and with the mean over the entropy map. The DSC, the 95th Hausdorff distance, the mean surface distance (MSD) and the surface DSC were computed for segmentation quality. Correlations between the studied metrics and model quality were assessed with the Pearson correlation coefficient (r). The area under the curve (AUC) was determined for detecting segmentations that require reviewing.
For both tasks, our metric (λ = 0.30) correlated more strongly with the segmentation quality than the mean over the entropy map (for surface DSC, r > 0.65 vs. r < 0.60). The AUC was above 0.84 for detecting MSD values above 2 mm.
Our metric correlated strongly with clinically relevant segmentation metrics and detected segmentations that required reviewing, indicating its potential for automatic quality assurance of radiotherapy target auto-segmentations.
现有的放射治疗自动分割质量保证方法仅关注平均模型熵与骰子相似系数（DSC）之间的相关性。我们确定了一个直接从网络输出得出的指标，并将其与轮廓准确性的临床相关指标相关联。
可获得宫颈癌近距离放疗大体肿瘤体积的磁共振成像自动分割（106次分割）以及直肠癌外照射放疗临床靶体积的自动分割（77次分割）。将nnU-Net二值化前的输出作为评分图。我们将一个指标定义为评分图中高于阈值（λ）的体素的平均值。与评分图上的均值和标准差以及熵图上的均值进行比较。计算DSC、第95百分位豪斯多夫距离、平均表面距离（MSD）和表面DSC以评估分割质量。使用皮尔逊相关系数（r）评估所研究指标与模型质量之间的相关性。确定曲线下面积（AUC）以检测需要复查的分割。
对于这两项任务，我们的指标（λ = 0.30）与分割质量的相关性比熵图上的均值更强（对于表面DSC，r > 0.65对r < 0.60）。检测MSD值高于2mm时，AUC高于0.84。
我们的指标与临床相关的分割指标密切相关，并能检测出需要复查的分割，表明其在放射治疗靶区自动分割的自动质量保证方面具有潜力。
