Abstract
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.