Abstract
Cell instance segmentation is critical to analyzing biomedical images, yet accurately distinguishing tightly touching cells remains a persistent challenge. Existing instance segmentation frameworks, including detection-based, contour-based, and distance mapping-based approaches, have made significant progress, but balancing model performance with computational efficiency remains an open problem. In this paper, we propose a novel cell instance segmentation method inspired by the four-color theorem. By conceptualizing cells as countries and tissues as oceans, we introduce a four-color encoding scheme that ensures adjacent instances receive distinct labels. This reformulation transforms instance segmentation into a constrained semantic segmentation problem with only four predicted classes, substantially simplifying the instance differentiation process. To solve the training instability caused by the non-uniqueness of four-color encoding, we design an asymptotic training strategy and encoding transformation method. Extensive experiments on various modes demonstrate our approach achieves state-of-the-art performance. The code is available at this https URL.
Abstract (translated)
细胞实例分割对于分析生物医学图像至关重要，但准确地区分紧密接触的细胞仍然是一个持久的挑战。现有的基于检测、轮廓和距离映射的方法虽然在实例分割框架方面取得了显著进展，但在保持模型性能的同时实现计算效率仍然是一项未解难题。在这篇论文中，我们提出了一种受四色定理启发的新颖细胞实例分割方法。通过将细胞视为国家并将组织视为海洋，我们引入了一个四色编码方案，确保相邻实例获得不同的标签。这种重新定义将实例分割问题转化为一个仅有四个预测类别的约束语义分割问题，从而大大简化了实例区分的过程。 为了应对由四色编码非唯一性导致的训练不稳定性问题，我们设计了一种渐近式训练策略和编码转换方法。在各种模式下的广泛实验表明，我们的方法达到了最先进的性能水平。代码可在提供的链接处获取（请将“this https URL”替换为实际链接）。
URL
https://arxiv.org/abs/2506.09724
