Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularly for ultrasound (US) imaging. The quality of US images heavily relies on carefully tuned acoustic parameters, which vary across sonographers, machines, and settings. To improve the generalizability on US images across domains, we propose MI-SegNet, a novel mutual information (MI) based framework to explicitly disentangle the anatomical and domain feature representations; therefore, robust domain-independent segmentation can be expected. Two encoders are employed to extract the relevant features for the disentanglement. The segmentation only uses the anatomical feature map for its prediction. In order to force the encoders to learn meaningful feature representations a cross-reconstruction method is used during training. Transformations, specific to either domain or anatomy are applied to guide the encoders in their respective feature extraction task. Additionally, any MI present in both feature maps is punished to further promote separate feature spaces. We validate the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines. Furthermore, we demonstrate the effectiveness of the proposed MI-SegNet serving as a pre-trained model by comparing it with state-of-the-art networks.

翻译：基于学习的医学图像分割在不同域间的泛化能力目前受到域偏移导致的性能下降限制，尤其在超声成像中表现突出。超声图像质量高度依赖于精细调节的声学参数，这些参数会因操作者、设备和设置而异。为提升跨域超声图像的泛化性，我们提出MI-SegNet——一种基于互信息的新型框架，通过显式解耦解剖特征与域特征表征，从而有望实现鲁棒的域无关分割。该框架采用两个编码器提取解耦所需的相关特征，分割任务仅利用解剖特征图进行预测。为迫使编码器学习有意义的特征表征，训练过程中采用交叉重建方法。通过施加特定于域或特定于解剖结构的变换，引导编码器完成各自的特征提取任务。此外，对两个特征图中存在的互信息施加惩罚以进一步促进特征空间分离。我们在多组不同参数和设备的超声数据集上验证了所提域无关分割方法的泛化性，并通过与现有最优网络的对比，证明了MI-SegNet作为预训练模型的有效性。