新发布胶质瘤C6细胞数据集，助力精准分割模型训练与评估
Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation
- 构建75张高分辨率相衬显微图像，含超1.2万细胞标注
- 提供细胞体和形态分类，支持癌症研究与模型验证
- 适合生物医学图像分析、细胞分割模型研发者使用
我们提出Glioma C6，一个用于胶质瘤C6细胞实例分割的新开放数据集，兼具基准测试与训练资源功能。数据集包含75张高分辨率相衬显微图像，涵盖超过12,000个标注细胞，为生物医学图像分析提供真实测试环境。数据包含细胞体标注及由生物学家提供的细胞形态分类信息，进一步基于形态学对细胞进行额外分类，以提升图像数据在癌症研究中的利用率。该数据集分为两部分：第一部分采用可控参数用于基准测试，第二部分用于在不同条件下评估模型泛化能力。我们评估了多个通用分割模型的性能，揭示其在本数据集上的局限性。实验表明，在Glioma C6上训练能显著提升分割性能，验证其对构建鲁棒、泛化能力强模型的价值。数据集已公开供研究人员使用。
原文摘要 · Abstract (English)
We present Glioma C6, a new open dataset for instance segmentation of glioma C6 cells, designed as both a benchmark and a training resource for deep learning models. The dataset comprises 75 high-resolution phase-contrast microscopy images with over 12,000 annotated cells, providing a realistic testbed for biomedical image analysis. It includes soma annotations and morphological cell categorization provided by biologists. Additional categorization of cells, based on morphology, aims to enhance the utilization of image data for cancer cell research. Glioma C6 consists of two parts: the first is curated with controlled parameters for benchmarking, while the second supports generalization testing under varying conditions. We evaluate the performance of several generalist segmentation models, highlighting their limitations on our dataset. Our experiments demonstrate that training on Glioma C6 significantly enhances segmentation performance, reinforcing its value for developing robust and generalizable models. The dataset is publicly available for researchers.
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。