用空间转录组数据替代人工标注，实现更高效精准的细胞核分析。
Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
- 利用空间转录组数据生成细胞类型标签和核掩码，作为图像分类的监督信号。
- 在未见器官上测试，仍达到更高分割准确率，展现强泛化能力。
- 适合缺乏标注数据的病理图像分析场景，尤其适用于多组织研究。
基于深度学习的病理图像细胞核分割与分类通常依赖大规模像素级人工标注，成本高且难以跨组织与染色条件获取。为解决此问题，本文提出一种框架，利用空间转录组（ST）数据作为监督信号进行细胞核分割与分类。通过整合细胞水平的ST数据，从组织病理图像中获得基因表达谱及对应核掩码；将基因表达谱转换为细胞类型标签，用于图像分类训练。由于现有基于基因表达的细胞分型方法不适用于图像识别，我们引入一种面向图像的分类方法，打通基因表达分型与图像分类之间的鸿沟。为评估泛化性能，我们在先前未见的器官上开展分割实验，并与传统监督模型对比。尽管训练仅覆盖较少器官类型，本框架仍实现更高分割准确率，证明其具备强迁移能力。分类实验进一步显示，相比现有方法有持续提升。
原文摘要 · Abstract (English)
Deep learning-based nuclei segmentation and classification in pathology images typically rely on large-scale pixel-level manual annotations, which are costly and difficult to obtain across diverse tissues and staining conditions. To address this limitation, we propose a framework that leverages spatial transcriptomics (ST) data as supervision for nuclei segmentation and classification. By incorporating cell-level ST data, we obtain gene expression profiles and corresponding nuclear masks from histopathological images. Gene expression profiles are converted into cell-type labels and used as training data for image-based classification. Because existing gene expression-based cell-type classification methods are not designed for image recognition, we introduce an image-oriented classification approach that bridges gene expression-based cell typing and image-based cell classification. To evaluate generalization, we conduct segmentation experiments on previously unseen organs and compare our method with conventional supervised models. Despite being trained on fewer organ types, our framework achieves higher segmentation accuracy, demonstrating strong transferability. Classification experiments further show consistent improvements over existing approaches.
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