通过图结构相似性正则化，提升半监督医学图像分割的准确性。
Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation
- 基于成对相似性正则化，对齐不同域图像的图结构特征。
- 在ACDC数据集上平均提升超过10.66%，优于现有先进方法。
- 适合需要减少标注依赖的医学图像分割场景。
充分利用未标注数据，半监督医学图像分割算法显著缓解了标注数据有限的问题，提升了分割精度。然而，标注与未标注数据之间的分布偏移削弱了标注数据的信息利用。为此，我们提出一种基于成对相似性正则化（PaSR）的图网络特征对齐方法，用于半监督医学图像分割。PaSR通过保持目标域与源域之间特征图的成对结构相似性一致性，对齐不同域图像的图结构，缓解医学图像中的分布偏移问题。同时，通过对齐图聚类信息，进一步提升教师网络中伪标签的准确性，增强模型的半监督效率。实验在三个医学图像分割基准数据集上进行，结果表明，在多种指标上均优于先进方法。在ACDC数据集上，平均提升超过10.66%。
原文摘要 · Abstract (English)
With fully leveraging the value of unlabeled data, semi-supervised medical image segmentation algorithms significantly reduces the limitation of limited labeled data, achieving a significant improvement in accuracy. However, the distributional shift between labeled and unlabeled data weakens the utilization of information from the labeled data. To alleviate the problem, we propose a graph network feature alignment method based on pairwise similarity regularization (PaSR) for semi-supervised medical image segmentation. PaSR aligns the graph structure of images in different domains by maintaining consistency in the pairwise structural similarity of feature graphs between the target domain and the source domain, reducing distribution shift issues in medical images. Meanwhile, further improving the accuracy of pseudo-labels in the teacher network by aligning graph clustering information to enhance the semi-supervised efficiency of the model. The experimental part was verified on three medical image segmentation benchmark datasets, with results showing improvements over advanced methods in various metrics. On the ACDC dataset, it achieved an average improvement of more than 10.66%.
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