# 每类细胞仅需一次点击：免训练的群体交互用于细胞实例分割

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-28 08:00
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmpv72eee047csl0z38hvn4r3
- 原文链接：https://arxiv.org/abs/2605.29429

## AI 摘要

传统细胞实例分割模型在分布外细胞类型上性能下降，交互式基础模型虽能解决但逐实例提示的成本过高。本文提出Group Prompting新范式，将交互成本从逐实例优化为逐类型。其核心是Chain-of-Prompts框架，利用冻结的Segment Anything Model图像编码器在特征空间中自然形成的细胞聚类特性，仅需为每种细胞类型提供一个用户点击，即可通过识别多尺度编码器特征中的可靠同类型位置，并迭代选择空间距离最远的可靠点作为新提示，从而分割该类型所有实例。该方法无需任何训练，在多个基准上，单个点击可保持逐实例性能的90%甚至99%以上。

## 正文

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/