每種細胞類型只需一次點擊：面向細胞實例分割的免訓練群組交互方法

摘要

在细胞特异性数据集上训练的细胞实例分割模型，在面对分布外细胞类型时会出现严重的性能下降，而交互式基础模型虽能通过逐实例提示克服这一问题，但其成本对于包含数百至数千个密集实例的组织病理学图像而言过高。我们提出“分群提示”（Group Prompting）这一新范式，将交互式分割从逐实例的 O(N) 转变为逐类型的 O(T)，即每个细胞类型仅需一次点击即可分割所有该类型的实例。我们的关键发现是，在给出任何提示之前，Segment Anything Model (SAM) 的冻结图像编码器已在其特征空间中自动聚集相同类型的细胞。利用这一特性，我们提出了零训练框架 Chain-of-Prompts (CoP)，该框架通过递归扩展单个用户点击来工作：（1）通过对多尺度编码器特征进行非参数门控，识别出可靠的同类位置；（2）选择空间距离最远的可靠点作为下一提示，以最大化覆盖范围。在三个带有细胞类型标注的基准测试中，CoP 每类只需一次点击即可保留超过 90% 的逐实例性能，并在无需任何额外训练的情况下超越了全监督方法。在四个形态学同质化基准测试中，单次点击即可保留超过 99% 的性能。项目页面：https://shjo-april.github.io/Chain-of-Prompts/

English

Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome this through per-instance prompting at a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of densely packed instances. We introduce Group Prompting, a new paradigm that shifts interactive segmentation from per-instance O(N) to per-type O(T), where a single click per cell type suffices to segment all instances of that type. Our key observation is that the frozen image encoder of the Segment Anything Model (SAM) already clusters same-type cells in its feature space before any prompt is given. Exploiting this property, we propose Chain-of-Prompts (CoP), a training-free framework that recursively expands a single user click by (1) identifying reliable same-type locations through non-parametric gating of multi-scale encoder features, and (2) selecting the most spatially distant reliable point as the next prompt to maximize coverage. On three cell-type-annotated benchmarks, CoP with one click per type retains over 90% of per-instance performance and surpasses fully-supervised methods without any additional training. On four morphologically homogeneous benchmarks, a single click retains over 99%. Project Page: https://shjo-april.github.io/Chain-of-Prompts/