根据参考细胞精准分割特定类型细胞，推动个性化细胞分析
Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation
- 基于DINOv2构建跨注意力框架，融合图像特征与参考嵌入
- 在1372张图像、超11万细胞上实现高精度类型特异性分割
- 适合生物医学图像分析、细胞分类研究者使用
准确的细胞分割对生物与医学成像研究至关重要。尽管深度学习模型已显著提升分割性能，但多数方法仅限于通用细胞分割，无法区分特定细胞类型。本文提出个性化细胞分割（PerCS）任务：给定一个参考细胞，分割出所有同类型细胞。为此，我们重构公开数据集，建立基准，涵盖1,372张图像和超过110,000个标注细胞。作为首个解决方案，我们提出PerCS-DINO框架，基于DINOv2骨干网络，通过交叉注意力变换器和对比学习融合图像特征与参考嵌入，有效实现与参考匹配的细胞分割。大量实验验证了PerCS-DINO的有效性，并揭示了该任务的挑战。我们期望PerCS成为推动细胞相关应用研究的重要测试平台。
原文摘要 · Abstract (English)
Accurate cell segmentation is critical for biological and medical imaging studies. Although recent deep learning models have advanced this task, most methods are limited to generic cell segmentation, lacking the ability to differentiate specific cell types. In this work, we introduce the Personalized Cell Segmentation (PerCS) task, which aims to segment all cells of a specific type given a reference cell. To support this task, we establish a benchmark by reorganizing publicly available datasets, yielding 1,372 images and over 110,000 annotated cells. As a pioneering solution, we propose PerCS-DINO, a framework built on the DINOv2 backbone. By integrating image features and reference embeddings via a cross-attention transformer and contrastive learning, PerCS-DINO effectively segments cells matching the reference. Extensive experiments demonstrate the effectiveness of the proposed PerCS-DINO and highlight the challenges of this new task. We expect PerCS to serve as a useful testbed for advancing research in cell-based applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。