Abstract
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.
Abstract (translated)
准确的细胞实例分割是数字病理学分析的基础。现有的基于轮廓检测和距离映射的方法在处理复杂且密集的细胞区域时仍然面临重大挑战。基于图着色的方法为这一任务提供了一种新的范式，但在现实场景中，尤其是在存在大量重叠和复杂拓扑结构的情况下，这种方法的有效性尚未得到验证。 为了应对这一问题，我们发布了大规模数据集GBC-FS 2025，该数据集中包含了高度复杂的密集亚细胞核排列。我们在四个不同的数据集上进行了首次关于细胞邻接图的色度特性的系统分析，并揭示了一个重要的发现：大多数现实世界的细胞图是非二部图，且含有大量的奇数长度循环（主要是三角形）。这表明简单的二着色理论不足以处理复杂组织的问题，而更高色度的模型会导致表示冗余和优化困难。 基于对这种复杂现实环境的理解，我们提出了一种新的方法Disco（密集重叠细胞实例分割通过邻接感知协作着色），这是一种基于“分治”原则并利用邻接信息的框架。这种方法独特地结合了数据驱动拓扑标签策略与受约束的深度学习系统，以解决复杂的邻接冲突问题。 首先，“显式标记”策略通过递归分解细胞图并将“冲突集”隔离出来，将拓扑挑战转化为可学习分类任务。 其次，“隐式消歧机制”通过强制不同实例之间的特征差异性来解析冲突区域中的模糊性，使模型能够学习到分离的特征表示。
URL
https://arxiv.org/abs/2602.05420
