Table_1_Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.docx

ObjectiveTo establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using diffusion-weighted imaging (DWI) and T1 weighted imaging (T1WI) images.MethodsThe model consisted of two 3D U-Net algorithms. A total of 859 patients with clinically suspected or confirmed PCa between January 2017 and December 2020 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. Then, 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from January to May 2021 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision, and F1-score were assessed at the lesion level.ResultsThe pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85, and the HD values were

研究目标：旨在建立与评估基于3D U-Net模型，用于前列腺癌（PCa）患者自动分割和检测盆腔骨转移的扩散加权成像（DWI）和T1加权成像（T1WI）图像。研究方法：该模型由两个3D U-Net算法构成。自2017年1月至2020年12月，共纳入859名临床疑似或确诊为PCa的患者，用于盆腔骨骼结构的首次3D U-Net分割开发。随后，选取了334名PCa患者用于骨转移分割模型开发。此外，从2021年1月至5月招募了63名患者进行网络的体外评估。网络开发采用DWI和T1WI图像作为输入。通过Dice相似系数（DSC）、体积相似度（VS）和Hausdorff距离（HD）评估分割性能。在患者层面，使用灵敏度、特异性和曲线下面积（AUC）评估检测性能；在病灶层面，评估召回率、精确度和F1分数。研究结果：在DWI和T1WI图像上对盆腔骨骼结构的分割，平均DSC和VS值均超过0.85，Hausdorff距离值（...）