论文标题
形状感知的元学习，用于将前列腺MRI分割概括为看不见的域
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
论文作者
论文摘要
域移位的模型泛化能力（例如，各种成像协议和扫描仪）对于现实世界中临床部署的深度学习方法至关重要。本文解决了域概括的具有挑战性的问题，即从多域源数据中学习模型，以便它可以直接推广到看不见的目标域。我们提出了一种新型的形状感知元学习方案，以改善前列腺MRI分割中的模型概括。我们的学习方案根源在基于梯度的元学习中，通过在训练过程中用虚拟元训练和元测试来明确模拟域转移。重要的是，考虑到将分割模型应用于看不见的域（即预测掩模的不完整形状和模棱两可的边界）时遇到的缺陷，我们进一步引入了两个互补的损失目标，以特别鼓励在模拟域下的分裂状态的平滑度和平稳性，以增强元观察到元观点。我们评估了来自六个不同机构的前列腺MRI数据的方法，并从公共数据集中获得了分配变化。实验结果表明，我们的方法的表现超过了在看不见域的所有六个环境中始终如一的最新概括方法。
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
