最优传输度量学习用于部分监督分割中的特征对齐

中文摘要 AI 辅助

多器官分割常面临部分标注数据集和不同成像来源间的域偏移挑战。为解决这些限制，我们提出一个两阶段学习框架，有效利用部分监督。在第一阶段，模型从可用标注中学习，对已标注器官产生准确分割，建立稳健的特征表示。在第二阶段，我们引入可学习的器官原型和Sinkhorn三元组损失，以强制跨数据集的器官级特征一致性。这促使同一器官的潜在嵌入保持接近，同时增加不同器官间的分离度，即使标注缺失时亦然。我们的方法在BTCV数据集上达到与最先进方法相当的性能，同时保持计算高效。通过显式对齐特征分布而非仅依赖伪标签，该框架有效缓解域偏移，使其特别适用于标注资源有限的医学图像分割任务。

英文摘要

Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limitations, we propose a two-stage learning framework that efficiently leverages partial supervision. In the first stage, the model learns from available annotations to produce accurate segmentations of annotated organs, establishing robust feature representations. In the second stage, we introduce learnable organ prototypes and a Sinkhorn-triplet loss to enforce organ-wise feature consistency across datasets. This encourages latent embeddings of the same organ to remain close, while increasing separation between different organs, even when annotations are missing. Our approach achieves performance comparable to state-of-the-art methods on the BTCV dataset, while remaining computationally efficient. By explicitly aligning feature distributions rather than relying solely on pseudo-labels, the framework effectively mitigates domain shift, making it particularly suitable for medical image segmentation tasks with limited annotation resources.

发表机构

• ML Collective

机构由 AI 辅助整理，请以论文原文为准。