Recent advancements in deep learning-based interactive segmentation methods have significantly improved pathology image segmentation. Most existing approaches utilize user-provided positive and negative clicks to guide the segmentation process. However, these methods primarily rely on iterative global updates for refinement, which lead to redundant re-prediction and often fail to capture fine-grained structures or correct subtle errors during localized adjustments. To address this limitation, we propose the Click-based Local Refinement (Clore) pipeline, a simple yet efficient method designed to enhance interactive segmentation. The key innovation of Clore lies in its hierarchical interaction paradigm: the initial clicks drive global segmentation to rapidly outline large target regions, while subsequent clicks progressively refine local details to achieve precise boundaries. This approach not only improves the ability to handle fine-grained segmentation tasks but also achieves high-quality results with fewer interactions. Experimental results on four datasets demonstrate that Clore achieves the best balance between segmentation accuracy and interaction cost, making it an effective solution for efficient and accurate interactive pathology image segmentation.

翻译：基于深度学习的交互式分割方法的最新进展显著提升了病理图像分割的性能。现有方法大多利用用户提供的正负点击引导分割过程。然而，这些方法主要依赖迭代式全局更新进行细化，导致冗余的重新预测，且在进行局部调整时，往往难以捕捉精细结构或修正细微错误。为解决这一局限，我们提出了基于点击的局部细化（Clore）流程——一种旨在增强交互式分割的简洁而高效的方法。Clore的关键创新在于其层次化交互范式：初始点击驱动全局分割，快速勾勒大面积目标区域；后续点击逐步细化局部细节，实现精确边界。该方法不仅提升了处理精细分割任务的能力，还能以更少的交互次数获得高质量结果。在四个数据集上的实验结果表明，Clore在分割精度与交互成本之间实现了最佳平衡，为高效与精确的交互式病理图像分割提供了一种有效解决方案。