利用体积先验与Wasserstein约束提升医学图像分割精度
Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
- 引入图像级和数据集级的体积先验，通过Wasserstein距离约束优化分割结果
- 在ACDC、PROMISE12等数据集上实现更优分割性能，显著提升小样本场景下的泛化能力
- 适合关注半监督医学图像分割、需融合解剖结构先验的研究者
尽管半监督医学图像分割已取得显著进展，但现有方法普遍忽视了特征提取的有效指导和数据集中的重要先验信息。本文提出一种半监督医学图像分割框架，有效整合空间正则化方法与体积先验。具体而言，将来自变分模型的图像级显式体积先验与阈值动力学空间正则化集成到主干分割网络中。通过回归网络估计每个未标注图像的目标区域体积，并利用图像级Wasserstein距离约束对主干网络进行正则化，确保每张未标注图像的分割结果类别比例与其预测体积一致。此外，设计基于弱隐式体积先验的数据集级Wasserstein损失函数，使未标注数据集预测的体积分布与标注数据集相近。在2017 ACDC数据集、PROMISE12数据集及大腿肌肉MR图像数据集上的实验表明，所提方法具有明显优势。
原文摘要 · Abstract (English)
Despite signi cant progress in semi-supervised medical image segmentation, most existing segmentation networks overlook e ective methodological guidance for feature extraction and important prior information from datasets. In this paper, we develop a semi-supervised medical image segmentation framework that e ectively integrates spatial regularization methods and volume priors. Speci cally, our approach integrates a strong explicit volume prior at the image scale and Threshold Dynamics spatial regularization, both derived from variational models, into the backbone segmentation network. The target region volumes for each unlabeled image are estimated by a regression network, which e ectively regularizes the backbone segmentation network through an image-scale Wasserstein distance constraint, ensuring that the class ratios in the segmentation results for each unlabeled image match those predicted by the regression network. Additionally, we design a dataset-scale Wasserstein distance loss function based on a weak implicit volume prior, which enforces that the volume distribution predicted for the unlabeled dataset is similar to that of labeled dataset. Experimental results on the 2017 ACDC dataset, PROMISE12 dataset, and thigh muscle MR image dataset show the superiority of the proposed method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。