Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these semantic classes. Nonetheless, they often fail to generalize when there is a significant domain (i.e., distributional) shift between the training (i.e., source) data and the dataset(s) encountered when deployed (i.e., target), necessitating manual annotations for the target data to achieve acceptable performance. This is especially important in medical imaging because different image modalities have significant intra- and inter-site variations due to protocol and vendor variability. Current techniques are sensitive to hyperparameter tuning and target dataset size. This paper presents an unsupervised domain adaptation approach for semantic segmentation that alleviates the need for annotating target data. Using kernel density estimation, we match the target data distribution to the source in the feature space, particularly when the number of target samples is limited (3% of the target dataset size). We demonstrate the efficacy of our proposed approach on 2 datasets, multisite prostate MRI and histopathology images.

翻译：语义分割是自动图像解读与分析中的关键步骤，其目标是将像素划分至一个或多个预定义的语义类别中。基于深度学习的语义分割方法依赖带标注图像的能力来学习表征这些语义类别的特征。然而，当训练数据（即源域）与部署过程中遇到的数据集（即目标域）之间存在显著的域（即分布）偏移时，这些方法往往难以泛化，需对目标数据进行人工标注才能达到可接受的性能。这一问题在医学影像领域尤为突出，因为不同成像模态由于采集协议和设备供应商的差异，在中心内部及中心之间均存在显著变异。现有技术对超参数调优和目标数据集规模较为敏感。本文提出一种针对语义分割的无监督域适应方法，无需对目标数据进行标注。我们利用核密度估计，在特征空间中将目标数据分布与源域数据分布进行匹配，尤其在目标样本数量有限（仅为目标数据集规模的3%）的情况下。我们在两个数据集（多中心前列腺MRI数据集和组织病理学图像数据集）上验证了所提方法的有效性。