Sunoqrot Mohammed R S, Selnæs Kirsten M, Sandsmark Elise, Nketiah Gabriel A, Zavala-Romero Olmo, Stoyanova Radka, Bathen Tone F, Elschot Mattijs
Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030 Trondheim, Norway.
Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway.
Diagnostics (Basel). 2020 Sep 18;10(9):714. doi: 10.3390/diagnostics10090714.
Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
与传统的磁共振成像（MRI）放射学读片相比，计算机辅助检测与诊断（CAD）系统有提高稳健性和效率的潜力。前列腺的全自动分割是前列腺癌CAD的关键步骤，但仍需要目视检查来检测分割不佳的病例。因此，本研究的目的是基于T2加权MRI建立一个前列腺分割的全自动质量控制（QC）系统。使用四种不同的基于深度学习的分割方法对585例患者的前列腺进行分割。从分割后的前列腺掩码中提取一阶、形状和纹理放射组学特征。与手动分割相比，为每个自动分割计算一个参考质量评分（QS）。在一个随机分配的训练数据集（N = 1756，每种分割方法439例）上训练并优化最小绝对收缩和选择算子（LASSO），以基于最能估计参考QS的放射组学特征建立一个可推广的线性回归模型。随后，该模型用于估计一个独立测试数据集（N = 584，每种分割方法146例）的QS。在0到100的量表上，估计的QS与参考QS之间的平均±标准差绝对误差为5.47±6.33。此外，我们发现估计的QS与参考QS之间有很强的相关性（rho = 0.70）。总之，我们开发了一个自动化QC系统，可能有助于评估自动前列腺分割的质量。
