Supporting data for "CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models"

In recent years, cell segmentation techniques have played a critical role in the analysis of biological images, especially for quantitative studies. Deep learning-based cell segmentation models have demonstrated remarkable performance in segmenting cell and nucleus boundaries, however, they are typically tailored to specific modalities or require manual tuning of hyperparameters, limiting their generalizability to unseen data. Comprehensive datasets that support both the training of universal models and the evaluation of various segmentation techniques are essential for overcoming these limitations and promoting the development of more versatile cell segmentation solutions. <br>Here, we present CellBinDB, a large-scale multimodal annotated dataset established for these purposes. CellBinDB contains more than 1,000 annotated images, each labeled to identify the boundaries of cells or nuclei, including 4,6-Diamidino-2-Phenylindole (DAPI), Single-stranded DNA (ssDNA), Hematoxylin and Eosin (H&amp;E), and Multiplex Immunofluorescence (mIF) staining, covering over 30 normal and diseased tissue types from human and mouse samples. Based on CellBinDB, we benchmarked eight state-of-the-art and widely used cell segmentation technologies/methods, and our further analysis reveals that complex cell shapes reduce segmentation accuracy while higher image gradients improve boundary detection, offering insights for refining segmentation strategies across diverse imaging scenarios.

近年来，细胞分割技术在生物图像分析，尤其是定量研究中发挥着至关重要的作用。基于深度学习的细胞分割模型在分割细胞与细胞核边界方面展现出优异性能，但这类模型通常仅适配特定成像模态，或需要手动调整超参数，这限制了它们对未见数据的泛化能力。能够同时支持通用模型训练与多种分割技术评估的高质量数据集，是克服上述局限、推动更通用细胞分割方案发展的核心要素。
为此，我们构建了CellBinDB——一个专为该目标打造的大规模多模态标注数据集。CellBinDB包含超过1000张标注图像，每张图像均标注有细胞或细胞核的边界，涵盖4,6-二脒基-2-苯基吲哚(4,6-Diamidino-2-Phenylindole, DAPI)、单链DNA(Single-stranded DNA, ssDNA)、苏木精-伊红(Hematoxylin and Eosin, H&E)染色与多重免疫荧光(Multiplex Immunofluorescence, mIF)等多种染色方式，覆盖源自人类与小鼠样本的30余种正常及病变组织类型。基于CellBinDB，我们对8种当前前沿且广泛应用的细胞分割技术/方法进行了基准测试；进一步分析表明，复杂的细胞形态会降低分割精度，而更高的图像梯度则有助于边界检测，这为优化不同成像场景下的分割策略提供了参考依据。