Abstract
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.
Abstract (translated)
学习型医学图像分割在不同领域的泛化能力目前受到域 shift 的影响,特别是对于超声波 (US) 成像的影响。超声波图像的质量很大程度上取决于精心调整的声学参数,这些参数在不同医生、设备和设置中有所不同。为了改善在不同领域的超声波图像上的泛化能力,我们提出了 MI-SegNet,一种基于互信息 (MI) 的新框架,以明确分离解剖学和域特征表示;因此,具有域独立的分割能力是可以期望的。我们使用两个编码器来提取相关的特征以进行分离。分割仅使用解剖学特征映射来进行预测。为了强迫编码器学习有意义的特征表示,在训练期间使用交叉重构方法。对于不同域或解剖学的特定变换,进行了应用,以指导编码器各自的特征提取任务。此外,在两个特征映射中都存在 MI 时,对其进行惩罚,以进一步促进独立的特征空间。我们验证了所提出的具有域独立性分割方法在不同参数和机器上的泛化能力,并比较了当前最先进的网络。我们还证明了所提出的 MI-SegNet 作为预训练模型的有效性,通过与当前最先进的网络进行比较。
URL
https://arxiv.org/abs/2303.12649
