子宫内膜癌 MR 图像的子宫分割对于妇科医生来说是一个有价值的诊断工具。然而，基于深度学习的子宫分割依赖于人工像素级标注，费时费力且主观性强。为了减少对像素级标注的依赖，提出了一种子宫内膜癌MRI切片的弱监督子宫分割方法，该方法仅需要涂鸦标签，并通过伪标签技术、指数测地距离损失和输入扰动策略进行增强。具体来说，通过动态混合双分支网络的两个输出生成伪标签，扩展监督信息并促进相互监督训练来解决由于监督不足而造成的限制。另一方面，考虑到子宫与周围组织灰度强度差异较大，引入指数测地距离损失来增强网络捕获子宫边缘的能力。加入输入扰动策略，适应子宫灵活多变的特点，进一步提高网络的分割性能。该方法在 135 例子宫内膜癌的 MRI 图像上进行了评估。与其他四种弱监督分割方法相比，该方法的性能最好，其平均DI、HD95、Recall、Precision、ADP分别为92.8%、11.632、92.7%、93.6%、6.5%，提高了2.1%，分别为9.144、0.6%、2.4%、2.9%。实验结果表明，该方法比其他弱监督方法更有效，并且达到了与完全监督方法相似的性能。版权所有 © 2023 Elsevier Ltd. 保留所有权利。

Uterine segmentation of endometrial cancer MR images can be a valuable diagnostic tool for gynecologists. However, uterine segmentation based on deep learning relies on artificial pixel-level annotation, which is time-consuming, laborious and subjective. To reduce the dependence on pixel-level annotation, a method of weakly supervised uterine segmentation on endometrial cancer MRI slices is proposed, which only requires scribble label and is enhanced by pseudo-label technology, exponential geodesic distance loss and input disturbance strategy. Specifically, the limitations caused by the shortage of supervision are addressed by dynamically mixing the two outputs of the dual branch network to generate pseudo-labels, expanding supervision information and promoting mutual supervision training. On the other hand, considering the large difference of grayscale intensity between the uterus and surrounding tissues, the exponential geodesic distance loss is introduced to enhance the ability of the network to capture the edge of the uterus. Input disturbance strategies are incorporated to adapt to the flexible and variable characteristics of the uterus and further improve the segmentation performance of the network. The proposed method is evaluated on MRI images from 135 cases of endometrial cancer. Compared with other four weakly supervised segmentation methods, the performance of the proposed method is the best, whose mean DI, HD95, Recall, Precision, ADP are 92.8%, 11.632, 92.7%, 93.6%, 6.5% and increasing by 2.1%, 9.144, 0.6%, 2.4%, 2.9% respectively. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.Copyright © 2023 Elsevier Ltd. All rights reserved.