Accurate cell instance segmentation is foundational for digital pathology analysis. Existing methods based on contour detection and distance mapping still face significant challenges in processing complex and dense cellular regions. Graph coloring-based methods provide a new paradigm for this task, yet the effectiveness of this paradigm in real-world scenarios with dense overlaps and complex topologies has not been verified. Addressing this issue, we release a large-scale dataset GBC-FS 2025, which contains highly complex and dense sub-cellular nuclear arrangements. We conduct the first systematic analysis of the chromatic properties of cell adjacency graphs across four diverse datasets and reveal an important discovery: most real-world cell graphs are non-bipartite, with a high prevalence of odd-length cycles (predominantly triangles). This makes simple 2-coloring theory insufficient for handling complex tissues, while higher-chromaticity models would cause representational redundancy and optimization difficulties. Building on this observation of complex real-world contexts, we propose Disco (Densely-overlapping Cell Instance Segmentation via Adjacency-aware COllaborative Coloring), an adjacency-aware framework based on the "divide and conquer" principle. It uniquely combines a data-driven topological labeling strategy with a constrained deep learning system to resolve complex adjacency conflicts. First, "Explicit Marking" strategy transforms the topological challenge into a learnable classification task by recursively decomposing the cell graph and isolating a "conflict set." Second, "Implicit Disambiguation" mechanism resolves ambiguities in conflict regions by enforcing feature dissimilarity between different instances, enabling the model to learn separable feature representations.

翻译：精确的细胞实例分割是数字病理学分析的基础。基于轮廓检测和距离映射的现有方法在处理复杂密集的细胞区域时仍面临重大挑战。基于图着色的方法为此任务提供了新范式，但该范式在具有密集重叠和复杂拓扑结构的真实场景中的有效性尚未得到验证。针对此问题，我们发布了大规模数据集GBC-FS 2025，其中包含高度复杂且密集的亚细胞核排列。我们首次对四个不同数据集的细胞邻接图着色特性进行了系统分析，并揭示了一个重要发现：大多数真实世界细胞图是非二部图，普遍存在奇数长度环（主要为三角形）。这使得简单的2-着色理论不足以处理复杂组织，而更高色数模型会导致表示冗余和优化困难。基于对复杂真实场景的这一观察，我们提出Disco（基于邻接感知协同着色的密集重叠细胞实例分割），这是一个基于"分而治之"原则的邻接感知框架。它独特地将数据驱动的拓扑标注策略与约束深度学习系统相结合，以解决复杂的邻接冲突。首先，"显式标注"策略通过递归分解细胞图并隔离"冲突集"，将拓扑挑战转化为可学习的分类任务。其次，"隐式消歧"机制通过强制不同实例间的特征差异性来消除冲突区域的歧义，使模型能够学习可分离的特征表示。