This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, easy to use, and maintenance. However, its successful implications demand properly annotated large number of diverse microscopy images, which has not been addressed properly— that confines the advanced image processing based plant cell research. Therefore, in this work, a low-cost microscopy image database of potato tuber cells having total 34,657 number of images, has been generated by Foldscope (costs around 1 USD) coupled with a smartphone. This dataset includes 13,369 unstained and 21,288 stained (safranin-o, toluidine blue-o, and lugol’s iodine) images with three-fold annotation based on weight, section areas, and tissue zones of the tubers. The physical image quality (e.g., contrast, focus, geometrical attributes, etc.) and its applicability in the DL framework (CNN-based multi-class and multi-label classification) have been examined and results are compared with the traditional microscope image set. The results show that the dataset is highly compatible for the DL framework.

中文翻译：

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用于深度学习框架的大规模全注释低成本显微图像数据集

这项工作提供了一个大规模的三重注释、低成本的马铃薯块茎显微镜图像数据集，用于深度学习 (DL) 框架中的植物细胞分析，这在植物细胞生物学研究的进步中具有巨大的潜力。事实上，低成本显微镜与新一代智能手机相结合，可以为基于 DL 的显微镜图像分析开辟新的领域，这提供了多种优势，包括便携性、易于使用和维护。然而，它的成功意义需要正确注释大量不同的显微镜图像，而这些图像尚未得到妥善解决——这限制了基于先进图像处理的植物细胞研究。因此，在这项工作中，马铃薯块茎细胞的低成本显微镜图像数据库共有 34,657 张图像，由 Foldscope（成本约 1 美元）和智能手机生成。该数据集包括 13,369 张未染色和 21,288 张染色（番红-o、甲苯胺蓝-o 和卢戈碘）图像，根据块茎的重量、切片面积和组织区域进行三重注释。已检查物理图像质量（例如，对比度、焦点、几何属性等）及其在 DL 框架（基于 CNN 的多类和多标签分类）中的适用性，并将结果与​​传统显微镜图像集进行比较. 结果表明，该数据集与 DL 框架高度兼容。已检查物理图像质量（例如，对比度、焦点、几何属性等）及其在 DL 框架（基于 CNN 的多类和多标签分类）中的适用性，并将结果与​​传统显微镜图像集进行比较. 结果表明，该数据集与 DL 框架高度兼容。已检查物理图像质量（例如，对比度、焦点、几何属性等）及其在 DL 框架（基于 CNN 的多类和多标签分类）中的适用性，并将结果与​​传统显微镜图像集进行比较. 结果表明，该数据集与 DL 框架高度兼容。