Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing, 100034, China.
Eur Radiol. 2021 Jul;31(7):5021-5031. doi: 10.1007/s00330-020-07608-9. Epub 2021 Jan 13.
To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU).
Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve.
The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst.
The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst.
• The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.
开发一种基于 3D U-Net 的深度学习模型，用于自动分割肾脏和肾肿瘤，并检测 CTU 皮髓质期的肾肿瘤。
使用来自 441 例肾肿瘤患者 CTU 数据的 882 个肾脏数据来学习和评估深度学习模型。使用 35 例小肾肿瘤（直径≤1.5cm）患者的 CTU 数据进行额外测试。手动在 CTU 的皮髓质期图像上标注肾脏、肾肿瘤和囊肿的真实数据。基于 3D U-Net 构建了肾脏和肾肿瘤的分割模型。通过 Dice 相似系数（DSC）评估分割准确性。模型预测分割的肾肿瘤和囊肿的最大三维感兴趣区体积的体积用作识别指标，通过接收者操作特征曲线下面积评估模型的检测性能。
提出的模型在肾脏和肾肿瘤的分割中表现出较高的准确性，平均 DSC 分别为 0.973 和 0.844。在肾脏囊肿的分割中表现中等，测试集的平均 DSC 为 0.536。此外，该模型在检测肾肿瘤和囊肿方面表现良好。
基于 3D U-Net 的自动分割和检测模型在肾脏和肾肿瘤的分割以及 CTU 皮髓质期肾肿瘤和囊肿的检测方面具有良好的效果。
基于 3D U-Net 的分割模型在肾脏和肾肿瘤的分割中表现出较高的准确性，在 CTU 皮髓质期对肾肿瘤和囊肿的检测具有良好的性能。
基于 3D U-Net 的分割模型是一种全自动辅助诊断工具，可用于减轻放射科医生的工作量并提高诊断的准确性。
基于 3D U-Net 的分割模型有助于为诊断、治疗、手术规划等提供定量信息。
