A Large-Scale Fully Annotated Foldscope Microscopy Image Dataset for Deep Learning Framework

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, ease of 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 a 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.

本研究构建了一款适用于深度学习（Deep Learning, DL）框架下植物细胞分析的大规模三重标注低成本马铃薯块茎显微图像数据集，其在推动植物细胞生物学研究进展方面具备巨大潜力。事实上，将低成本显微镜与新一代智能手机相结合，可为基于深度学习的显微图像分析开辟全新应用维度，该方案兼具便携性强、操作简便、维护简易等多重优势。然而，此类方案的成功落地亟需大量标注规范且类型多样的显微图像，但目前该需求尚未得到妥善满足，这也制约了基于先进图像处理技术的植物细胞研究发展。为此，本研究通过搭配智能手机的Foldscope折叠显微镜（单台成本约1美元），构建了总计34657张图像的马铃薯块茎细胞显微图像数据库。该数据集包含13369张未染色图像与21288张染色图像，染色试剂涵盖番红O、甲苯胺蓝O及卢戈氏碘液；数据集基于马铃薯块茎的重量、切片面积与组织区域完成三重标注。

搜集汇总

背景与挑战

背景概述

该数据集是一个大规模、完全标注的低成本Foldscope显微镜马铃薯块茎图像数据集，专为深度学习框架设计，包含34,657张图像（13,369张未染色和21,288张染色图像）。其特点在于使用约1美元的Foldscope结合智能手机生成，并基于块茎的重量、切片区域和组织区域进行三重标注，旨在促进植物细胞生物学研究和高级图像处理应用。