深度学习模型需要大规模训练以确保良好的性能，但在医学影像领域中获取带有注释的数据集具有挑战性。弱标注已被提出作为一种节省时间和精力的方法。本研究旨在使用可靠的性能，通过弱标注开发一个用于动态增强的磁共振成像（医学影像）中3D乳腺癌分割的深度学习模型。回顾性研究。从一个单一机构中选取736例乳腺癌患者，分为开发集（N = 544）和测试集（N = 192）。采用3.0-T、3D脂肪饱和梯度回波轴向T1加权快闪3D体积插值脑检查（VIBE）序列。两位放射学家通过边界框进行弱标注，然后依此完成了地真标注的自动和手动校正。使用此标注数据集训练了基于3D U-Net转换器（UNETR）的深度学习模型。通过定量和定性方法分析了测试集的分割结果，并将区域划分为整个乳房和边界框内的感兴趣区域（ROI）。作为定量方法，我们使用Dice相似系数评估分割结果。与地真体积的体积相关性使用Spearman相关系数评估。定性上，三名读者独立评估四个等级的视觉评分。 P值<0.05被认为具有统计学意义。我们开发的深度学习模型在整个乳房和ROI方面分别达到了0.75和0.89的中位Dice相似度得分。与地真体积的体积相关系数分别为0.82和0.86。通过三名读者的平均视觉评分为3.4。提出的基于弱标注的深度学习模型可以在使用DCE-MRI对乳腺癌进行3D分割时显示出良好的性能。技术效力：第2阶段。© 2023年国际磁共振医学学会。

Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.To develop a deep learning model for 3D breast cancer segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using weak annotation with reliable performance.Retrospective.Seven hundred and thirty-six women with breast cancer from a single institution, divided into the development (N = 544) and test dataset (N = 192).3.0-T, 3D fat-saturated gradient-echo axial T1-weighted flash 3D volumetric interpolated brain examination (VIBE) sequences.Two radiologists performed a weak annotation of the ground truth using bounding boxes. Based on this, the ground truth annotation was completed through autonomic and manual correction. The deep learning model using 3D U-Net transformer (UNETR) was trained with this annotated dataset. The segmentation results of the test set were analyzed by quantitative and qualitative methods, and the regions were divided into whole breast and region of interest (ROI) within the bounding box.As a quantitative method, we used the Dice similarity coefficient to evaluate the segmentation result. The volume correlation with the ground truth was evaluated with the Spearman correlation coefficient. Qualitatively, three readers independently evaluated the visual score in four scales. A P-value <0.05 was considered statistically significant.The deep learning model we developed achieved a median Dice similarity score of 0.75 and 0.89 for the whole breast and ROI, respectively. The volume correlation coefficient with respect to the ground truth volume was 0.82 and 0.86 for the whole breast and ROI, respectively. The mean visual score, as evaluated by three readers, was 3.4.The proposed deep learning model with weak annotation may show good performance for 3D segmentations of breast cancer using DCE-MRI.3 TECHNICAL EFFICACY: Stage 2.© 2023 International Society for Magnetic Resonance in Medicine.