Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring

2026-02-05

AI Summary

This study addresses the challenge of instance segmentation in densely packed, overlapping cells within complex tissues, which stems primarily from the prevalent non-bipartite structure and odd cycles in real-world cell adjacency graphs. To tackle this, the authors propose an adjacency-aware cooperative coloring framework that, for the first time, systematically analyzes the graph coloring properties of actual cellular arrangements and reveals their intrinsic non-bipartiteness. The method introduces a novel paradigm integrating topological labeling with constrained deep learning, achieving joint optimization through data-driven topological markers, explicit conflict set decomposition, and implicit feature-difference constraints. Evaluated on multiple high-density cell datasets—including GBC-FS 2025—the approach demonstrates significant improvements in segmentation accuracy.

Technology Category

• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• cell instance segmentation
• dense overlapping
• complex topology
• graph coloring
• digital pathology

Innovation

• cell instance segmentation
• graph coloring
• adjacency-aware
• topological labeling
• dense overlap