Segmentation is a key analysis tasks in biomedical imaging. Given the many different experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a promising remedy: it trains a model for a given task on a source dataset with labels and adapts it to a target dataset without additional labels. We introduce a probabilistic domain adaptation method, building on self-training approaches and the Probabilistic UNet. We use the latter to sample multiple segmentation hypothesis to implement better pseudo-label filtering. We further study joint and separate source-target training strategies and evaluate our method on three challenging domain adaptation tasks for biomedical segmentation.

翻译：分割是生物医学成像中的关键分析任务。鉴于该领域存在多种不同的实验设置，泛化能力的不足限制了深度学习在实践中的应用。领域自适应是一种有前景的补救方法：它利用带标签的源数据集训练特定任务模型，并使其适应无额外标签的目标数据集。我们提出了一种概率领域自适应方法，该方法基于自训练策略与概率UNet架构。通过后者采样多个分割假设，以实施更优的伪标签筛选。我们进一步研究了联合与分离的源-目标训练策略，并在三项具有挑战性的生物医学分割领域自适应任务上评估了该方法。