In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images. Since there currently lack methods particularly for UDA instance segmentation, we first design a Domain Adaptive Mask R-CNN (DAM) as the baseline, with cross-domain feature alignment at the image and instance levels. In addition to the image- and instance-level domain discrepancy, there also exists domain bias at the semantic level in the contextual information. Next, we, therefore, design a semantic segmentation branch with a domain discriminator to bridge the domain gap at the contextual level. By integrating the semantic- and instance-level feature adaptation, our method aligns the cross-domain features at the panoptic level. Third, we propose a task re-weighting mechanism to assign trade-off weights for the detection and segmentation loss functions. The task re-weighting mechanism solves the domain bias issue by alleviating the task learning for some iterations when the features contain source-specific factors. Furthermore, we design a feature similarity maximization mechanism to facilitate instance-level feature adaptation from the perspective of representational learning. Different from the typical feature alignment methods, our feature similarity maximization mechanism separates the domain-invariant and domain-specific features by enlarging their feature distribution dependency. Experimental results on three UDA instance segmentation scenarios with five datasets demonstrate the effectiveness of our proposed PDAM method, which outperforms state-of-the-art UDA methods by a large margin.

中文翻译：

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PDAM：显微图像中无监督域自适应实例分割的全景级特征对齐框架。

在这项工作中，我们提出了一种用于显微镜图像中非监督实例分割的无监督域自适应（UDA）方法，称为全景域自适应掩码R-CNN（PDAM）。由于目前缺乏专门用于UDA实例分割的方法，因此我们首先设计一个域自适应掩码R-CNN（DAM）作为基线，并在图像和实例级别进行跨域特征对齐。除了图像和实例级别的域差异外，上下文信息在语义级别还存在域偏差。因此，接下来，我们设计一个带有域区分符的语义分段分支，以在上下文级别弥合域差距。通过集成语义和实例级别的特征适配，我们的方法在全景级别对齐了跨域特征。第三，我们提出了一种任务重新加权机制，为检测和分段损失函数分配权衡权重。当特征包含特定于源的因素时，任务重新加权机制通过减轻某些迭代的任务学习来解决域偏差问题。此外，我们设计了一种特征相似度最大化机制，以从表示学习的角度促进实例级特征的适应。与典型的特征对齐方法不同，我们的特征相似度最大化机制通过扩大特征不变性来区分领域不变特征和领域特定特征。在具有五个数据集的三个UDA实例分割场景中的实验结果证明了我们提出的PDAM方法的有效性，