通过约束特征空间提升医学图像分割的泛化能力
SegReg: Latent Space Regularization for Improved Medical Image Segmentation
- 在U-Net特征图上施加隐空间正则化，鼓励结构化嵌入
- 在前列腺、心脏和海马体分割任务中实现稳定性能提升
- 无需额外参数即可增强持续学习中的任务迁移能力
医学图像分割模型通常使用体素级损失来约束输出空间，但对隐层特征表示缺乏约束，可能限制泛化能力。本文提出SegReg，一种作用于U-Net特征图的隐空间正则化框架，旨在鼓励结构化嵌入，同时与标准分割损失完全兼容。集成至nnU-Net框架后，在前列腺、心脏和海马体分割任务上均展现出一致的域泛化性能提升。此外，显式隐空间正则化可减少持续学习中的任务漂移，提升跨任务前向迁移能力，且无需增加记忆或额外参数。结果表明，隐空间正则化是构建更泛化、支持持续学习模型的实用方法。
原文摘要 · Abstract (English)
Medical image segmentation models are typically optimised with voxel-wise losses that constrain predictions only in the output space. This leaves latent feature representations largely unconstrained, potentially limiting generalisation. We propose {SegReg}, a latent-space regularisation framework that operates on feature maps of U-Net models to encourage structured embeddings while remaining fully compatible with standard segmentation losses. Integrated with the nnU-Net framework, we evaluate SegReg on prostate, cardiac, and hippocampus segmentation and demonstrate consistent improvements in domain generalisation. Furthermore, we show that explicit latent regularisation improves continual learning by reducing task drift and enhancing forward transfer across sequential tasks without adding memory or any extra parameters. These results highlight latent-space regularisation as a practical approach for building more generalisable and continual-learning-ready models.
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