Deep neural networks are commonly used for automated medical image
segmentation, but models will frequently struggle to generalize well across
different imaging modalities. This issue is particularly problematic due to the
limited availability of annotated data, making it difficult to deploy these
models on a larger scale. To overcome these challenges, we propose a new
semi-supervised training strategy called MoDATTS. Our approach is designed for
accurate cross-modality 3D tumor segmentation on unpaired bi-modal datasets. An
image-to-image translation strategy between imaging modalities is used to
produce annotated pseudo-target volumes and improve generalization to the
unannotated target modality. We also use powerful vision transformer
architectures and introduce an iterative self-training procedure to further
close the domain gap between modalities. MoDATTS additionally allows the
possibility to extend the training to unannotated target data by exploiting
image-level labels with an unsupervised objective that encourages the model to
perform 3D diseased-to-healthy translation by disentangling tumors from the
background. The proposed model achieves superior performance compared to other
methods from participating teams in the CrossMoDA 2022 challenge, as evidenced
by its reported top Dice score of 0.87+/-0.04 for the VS segmentation. MoDATTS
also yields consistent improvements in Dice scores over baselines on a
cross-modality brain tumor segmentation task composed of four different
contrasts from the BraTS 2020 challenge dataset, where 95% of a target
supervised model performance is reached. We report that 99% and 100% of this
maximum performance can be attained if 20% and 50% of the target data is
additionally annotated, which further demonstrates that MoDATTS can be
leveraged to reduce the annotation burden.

我们提出了一种名为 MoDATTS 的新的半监督训练策略，用于处理自动医学图像分割中存在的模态遗传性问题，并通过图像转换和视觉转换器实现更准确的肿瘤分割。该模型在 CrossMoDA 2022 挑战中表现出优越的性能，并在 BraTS 2020 挑战数据集上显示出一致的改进。