Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.

中文翻译：

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LCC-Net：用于半监督心脏 MR 图像分割的轻量级交叉一致性网络


语义分割在心脏磁共振（MR）图像分析中起着至关重要的作用。尽管有监督的深度学习方法取得了显着的性能改进，但它们高度依赖大量的像素级注释数据，而这些数据在临床实践中通常是不可用的。此外，性能最好的方法通常具有大量参数，这导致模型训练和测试的计算复杂度很高。这项研究通过名为 LCC-Net 的轻量级交叉一致性网络解决了在标记数据很少的情况下的心脏图像分割问题。具体来说，为了降低小型标记数据集过度拟合的风险，我们用轻量级模块代替计算密集型标准卷积。为了利用大量未标记的数据，我们引入了极端一致性学习，它对输入图像的不同扰动版本的预测强制执行等变约束。切割和混合不同的训练图像，作为对标记和未标记数据的极端扰动，用于增强鲁棒的表示学习。广泛的比较表明，所提出的模型显示出具有高注释和计算效率的良好性能。仅用两个带注释的主题进行模型训练，LCC-Net 的平均 Dice 性能比从头开始训练的基线 U-Net 提高了 14.4%。