3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solve this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the segmented objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from visual objects. Furthermore, we propose the replacement of the commonly used mean squared error (MSE) semi-supervised loss by a new Cross-model confident Binary Cross entropy (CBC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. We also extend CutMix augmentation to 3D SSL to further improve generalisation. Our TraCoCo shows state-of-the-art results for the Left Atrium (LA) and Brain Tumor Segmentation (BRaTS19) datasets with different backbones. Our code is available at https://github.com/yyliu01/TraCoCo.

翻译：三维医学图像分割方法已取得显著成功，但这类方法对大量体素级标注数据的依赖是一个缺点，鉴于获取此类标注的高昂成本，这一问题亟待解决。半监督学习通过使用大量无标注数据和少量有标注数据训练模型来应对该挑战。最成功的半监督学习方法基于一致性学习，通过最小化从无标注数据扰动视图获得的模型响应之间的距离。这些扰动通常保持视图之间的空间输入上下文相对一致，可能导致模型从空间输入上下文而非分割对象中学习分割模式。本文提出翻译一致性协同训练（TraCoCo），这是一种一致性学习半监督方法，通过改变输入数据视图的空间上下文进行扰动，使模型能从视觉对象中学习分割模式。此外，我们提出用新型交叉模型置信二元交叉熵损失替代常用的均方误差半监督损失，该损失能改善训练收敛性并保持对协同训练伪标签错误的鲁棒性。我们还将CutMix数据增强扩展到三维半监督学习以进一步提升泛化能力。TraCoCo在左心房和脑肿瘤分割数据集上使用不同骨干网络均达到了最先进水平。我们的代码已开源在https://github.com/yyliu01/TraCoCo。