From the Department of Radiological Sciences, University of California, Irvine, 101 The City Drive South, Building 55, Suite 201, Orange, CA 92868 (M.B., R.H., K.T.H., C. Chahine, M.R.); Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, Calif (C. Chantaduly, D.C., P.C.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (A.U.).
Radiol Imaging Cancer. 2021 May;3(3):e200024. doi: 10.1148/rycan.2021200024.
Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ. MRI, Genital/Reproductive, Prostate, Neural Networks © RSNA, 2021.
开发一种深度学习模型，以描绘磁共振图像上的前列腺移行区（TZ）和外周区（PZ）。
本回顾性研究纳入了 2013 年 1 月至 2016 年 5 月间接受多参数前列腺 MRI 检查和 MRI/经直肠超声融合活检的患者。一名经过委员会认证的腹部放射科医生手动对整个数据集进行前列腺、TZ 和 PZ 的分割。入组的患者被分为 60%的训练集、20%的验证集和 20%的测试集，用于模型开发。采用 U-Net 架构的三个卷积神经网络进行训练，以自动识别前列腺器官、TZ 和 PZ。使用 Dice 评分和 Pearson 相关系数评估分割模型的性能。
共纳入 242 例患者（242 例 MRI 图像；共 6292 例图像）。对前列腺器官、TZ 和 PZ 的分割模型进行了训练和验证。使用测试数据集，对于前列腺器官分割，平均 Dice 评分 0.940（四分位间距，0.930-0.961），体积 Pearson 相关系数为 0.981（95%CI：0.966，0.989）。对于 TZ 分割，平均 Dice 评分 0.910（四分位间距，0.894-0.938），体积 Pearson 相关系数为 0.992（95%CI：0.985，0.995）。对于 PZ 分割，平均 Dice 评分 0.774（四分位间距，0.727-0.832），体积 Pearson 相关系数为 0.927（95%CI：0.870，0.957）。
基于三个 U-Net 架构的深度学习可以准确分割前列腺、TZ 和 PZ。
