每类细胞一键足矣：免训练的组交互用于细胞实例分割

摘要

在特定细胞类型上训练的细胞实例分割模型，当面对分布外的细胞类型时性能会大幅下降，而交互式基础模型虽能通过逐实例提示克服这一局限，但对于包含数百至数千个密集实例的组织病理学图像而言，其成本过高。我们提出**组提示**（Group Prompting）这一新范式，将交互式分割从逐实例的 O(N) 复杂度转变为逐类型的 O(T) 复杂度——只需对每种细胞类型进行一次点击，即可分割该类型的所有实例。我们的关键发现是：Segment Anything Model (SAM) 的冻结图像编码器在未收到任何提示前，其特征空间中已自动实现了同类型细胞的聚类。利用这一特性，我们提出**提示链**（Chain-of-Prompts, CoP）这一免训练框架，通过以下方式递归地扩展单个用户点击：（1）利用多尺度编码器特征的非参数化门控机制识别可靠的同类型位置；（2）选取空间距离最远的可靠点作为下一个提示，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 以每类型一次点击保留了超过 90% 的逐实例性能，并在无需额外训练的情况下超越了全监督方法。在四个形态上同质的基准测试中，单次点击保留了超过 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/