RCPS：用于半监督医学图像分割的校正对比伪监督方法
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
AI总结：
针对半监督医学图像分割中伪标签噪声大、特征类别可分性不足的问题，提出结合校正伪监督与双向体素对比学习的RCPS方法，经多数据集验证性能优于现有最优方法。
摘要：
医学图像分割方法通常被设计为全监督模式以保证模型性能，这需要大量专家标注样本，而这类样本成本高且标注费力。半监督图像分割可通过利用大量未标注图像及有限的标注图像来缓解这一问题。然而，由于伪标签中存在潜在噪声且特征空间中的类别可分性不足，从大量未标注图像中学习鲁棒的表征仍然具有挑战性，这会削弱当前半监督分割方法的性能。为解决上述问题，我们提出了一种名为校正对比伪监督（Rectified Contrastive Pseudo Supervision，RCPS）的新型半监督分割方法，该方法结合了校正伪监督与体素级对比学习，以提升半监督分割的有效性。具体而言，我们基于不确定性估计和一致性正则化，为伪监督方法设计了一种新颖的校正策略，以降低伪标签中的噪声影响。此外，我们向网络中引入了双向体素对比损失，以确保特征空间中的类内一致性和类间对比性，从而提升分割任务中的类别可分性。我们在两个公开数据集和一个内部临床数据集上对所提出的RCPS分割方法进行了验证。实验结果表明，与半监督医学图像分割领域的现有最优方法相比，所提方法取得了更优的分割性能。源代码可在https://github.com/hsiangyuzhao/RCPS获取。
英文摘要：
Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
