A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment

Abstract

Abstract We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o, toluidine blue-o, and lugol’s-iodine) images with three-fold annotation including physical, morphological, and tissue grading based on weight, different section area, and tissue zone respectively. In addition, we prepared ground truth segmentation labels for three different tuber weights. We have validated the pertinence of annotations by performing multi-label cell classification, employing convolutional neural network (CNN), VGG16, for unstained and stained images. The accuracy has been achieved up to 0.94, while, F2-score reaches to 0.92. Furthermore, the ground truth labels have been verified by semantic segmentation algorithm using UNet architecture which presents the mean intersection of union up to 0.70. Hence, the overall results show that the data are very much efficient and could enrich the domain of microscopy plant cell analysis for DL-framework.

Plant phenotyping relevance

ジャガイモ塊茎の細胞形態・組織特性を対象とする大規模画像データセットを構築し、分類・セグメンテーションで検証しており、植物フェノタイピング用データ資源が中心である。

Code and data availability

The paper's potato tuber microscopy image dataset (raw stained/unstained images plus ground truth segmentation labels) is publicly deposited on figshare by the authors.