学习跨子分区泛化以实现感知异质性的领域自适应细胞核分割
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
AI总结：
针对组织病理图像中领域内异质性导致的自适应分割难题，提出两阶段解耦OCDA框架，结合子分区正则化与双分支形状结构保持模块，在跨模态和跨染色场景中优于现有方法。
摘要：
标注稀缺以及跨模态/染色剂的数据分布偏移，是阻碍深度学习模型应用于细胞核分析的两大障碍，而细胞核分析在数字病理学中具有广泛的潜在应用。近来，研究者提出无监督领域自适应（UDA）方法，以缓解不同成像模态之间的分布差距，从而在组织病理学图像中进行无监督细胞核分割。然而，现有UDA方法建立在每个领域内部的数据分布应当均匀这一假设之上。基于这一过度简化的假设，它们主张将组织病理学目标领域与源领域整体对齐，却忽视了由混合癌症类型和采样器官导致的、跨子分区的严重领域内差异。本文中，我们首次提出显式考虑组织病理学领域内部的异质性，并引入开放复合领域自适应（OCDA）来解决这一关键问题。具体而言，我们提出一种两阶段解耦框架，以在图像级和实例级获得领域不变的特征表示。这一整体设计解决了现有OCDA方法难以捕捉实例级变化的局限。本文还专门设计了两种正则化策略，以利用组织病理学图像中丰富的子分区特有特征，并促进子领域分解。此外，我们提出一个双分支细胞核形状与结构保持模块，以防止合成图像中出现细胞核过度生成和变形。在多种不同数据集上的跨模态和跨染色场景实验结果表明，与最先进的UDA和OCDA方法相比，我们的方法具有优越性。
英文摘要：
Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a broad spectrum of potential applications in digital pathology. Recently, unsupervised domain adaptation (UDA) methods have been proposed to mitigate the distributional gap between different imaging modalities for unsupervised nuclei segmentation in histopathology images. However, existing UDA methods are built upon the assumption that data distributions within each domain should be uniform. Based on the over-simplified supposition, they propose to align the histopathology target domain with the source domain integrally, neglecting severe intra-domain discrepancy over subpartitions incurred by mixed cancer types and sampling organs. In this paper, for the first time, we propose to explicitly consider the heterogeneity within the histopathology domain and introduce open compound domain adaptation (OCDA) to resolve the crux. In specific, a two-stage disentanglement framework is proposed to acquire domain-invariant feature representations at both image and instance levels. The holistic design addresses the limitations of existing OCDA approaches which struggle to capture instance-wise variations. Two regularization strategies are specifically devised herein to leverage the rich subpartition-specific characteristics in histopathology images and facilitate subdomain decomposition. Moreover, we propose a dual-branch nucleus shape and structure preserving module to prevent nucleus over-generation and deformation in the synthesized images. Experimental results on both cross-modality and cross-stain scenarios over a broad range of diverse datasets demonstrate the superiority of our method compared with state-of-the-art UDA and OCDA methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
