Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'

Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset: <strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): . <strong>Description of the dataset</strong> A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (Gridrec_reconstruction_downsized.zip) and the paganin, or phase-contrast, algortithm (Phase_contrast_reconstruction_downsized.zip). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension. A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (Binary_stack_for_local_thickness.zip), a map of the local thickness is created (Local_thickness_map.zip). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack. Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (Hand_labelled_slices.tif). Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (Fullstack_Prediction_Example-6_training_slices-6...). The source code for the segmentation program is available here, and the source code for the testing used in the paper is available here.本数据集用于论文《基于X射线显微计算机断层扫描与机器学习的叶片三维数字化解构》（Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning），请引用该数据集的发表文献：<strong>引用信息：</strong>Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, 及 J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. <em>Applications in Plant Sciences</em> 8(7): . <strong>数据集说明：</strong>采自奥地利图尔诺BOKU实验葡萄园的一株‘赤霞珠’葡萄（<em>Vitis vinifera</em> L.）的叶片，于瑞士光源（Swiss Light Source）使用X射线显微计算机断层扫描（X-ray microcomputed Tomography，microCT）完成扫描。扫描的原始重建结果分别采用gridrec算法（Gridrec_reconstruction_downsized.zip）与paganin算法（亦称相位衬度算法，Phase_contrast_reconstruction_downsized.zip）生成。为便于自动化图像分割，图像在x、y维度的尺寸均被减半，因此该维度下像素尺寸为0.325 µm，而z（切片）维度的像素尺寸为0.1625 µm。针对gridrec与相位衬度两类重建栈，分别生成了分割叶片细胞与胞间隙的二值图像，并将二者合并为Binary_stack_for_local_thickness.zip，同时生成了局部厚度映射图（Local_thickness_map.zip）。该映射图可反映二值图像栈中被标记为细胞的像素的最大直径。手动标注切片（即真值标签）绘制于以下切片序号：80、140、200、260、340、400、440、540、620、740、800、860、940、1060、1140、1240、1300、1400、1480、1540、1600、1690、1740、1840（对应文件Hand_labelled_slices.tif）。利用手动标注切片与各类图像数据，训练了随机森林模型，可自动完成重建栈剩余切片的分割（Fullstack_Prediction_Example-6_training_slices-6...）。分割程序的源代码与本文所用测试代码均可于此处获取。