A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 5 of 5
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<strong>Summary</strong> This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total. <strong>Parts</strong> The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: 10.5281/zenodo.5866228<br> Part 2 of 5: 029-056: 10.5281/zenodo.5866322<br> Part 3 of 5: 057-084: 10.5281/zenodo.5866363<br> Part 4 of 5: 085-111: 10.5281/zenodo.5866365<br> Part 5 of 5: 112-131: 10.5281/zenodo.5866367 <strong>(this upload)</strong> <strong>Description</strong> <em>Sample information</em> The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample. <em>Apparatus</em> The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020]. <em>Scanning setup</em> For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection. <em>Experimental plan</em> This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022]. <em>Technical details</em> All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB. The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: https://zenodo.org/record/5681008 <strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: https://www.cwi.nl/research/groups/computational-imaging <strong>Contact details</strong><br> zeegers [at] cwi [dot] nl <strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory. <br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018. If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.
<strong>摘要</strong> 本提交包包含131份针对建模黏土（培乐多(Play-Doh)）的计算机断层扫描（Computed Tomography, CT）结果，其中嵌入了不同数量的碎石。本提交包作为原始补充材料，用于复现题为《面向X射线异物检测的深度学习断层扫描工作流》[Zeegers 2022]一文中的CT重建及后续实验结果。本提交包整体共计包含3个部分。 <strong>数据集组成</strong> 本次提交的131份CT扫描数据被划分为5个独立的提交包：<br> 第1部分（共5部分）<em>:</em> 001-028，链接：10.5281/zenodo.5866228<br> 第2部分（共5部分）：029-056，链接：10.5281/zenodo.5866322<br> 第3部分（共5部分）：057-084，链接：10.5281/zenodo.5866363<br> 第4部分（共5部分）：085-111，链接：10.5281/zenodo.5866365<br> 第5部分（共5部分）：112-131，<strong>(本次上传)</strong> <strong>数据集说明</strong> <em>样本信息</em> 本次实验所用样本为建模黏土（培乐多(Play-Doh)，孩之宝(Hasbro)出品，产自美国罗德岛州），样本中嵌入了不同数量的碎石。共计制备131份样本，其中20份样本嵌入5~8颗碎石，3份样本嵌入3颗碎石，35份样本嵌入2颗碎石，62份样本嵌入1颗碎石，另有11份样本未嵌入任何碎石。碎石的平均直径约为7mm（直径范围3mm~11mm）。每份样本均使用全新塑形的培乐多黏土制作。 <em>采集设备</em> 本数据集采集于由TESCAN-XRE开发的FleX-ray实验室（FleX-ray Laboratory），该实验室坐落于阿姆斯特丹的荷兰数学与计算机科学研究中心（Centrum Wiskunde & Informatica, CWI）。本次使用的CT扫描仪包含锥束微焦点多色X射线点光源，以及一块1944×1536像素、14位灰度的平板探测器面板（Dexela1512NDT）。详细参数可参考文献[Coban 2020]。 <em>扫描设置</em> 对每份样本，以连续圆周运动旋转360°，共采集1800张射线投影图像。扫描参数设置为：管电压峰值90kV，目标功率20W。射线源与探测器间距为69.80cm，射线源与样本间距为44.14cm。单张投影的曝光时长为20ms。 <em>实验方案</em> 本数据集是针对X射线异物检测的监督式机器学习（supervised machine learning）标注数据采集工作流的演示成果。通过以相同采集角度进行断层重建、图像分割与虚拟投影，可获取样本的真值标注（ground truth）位置。获取训练数据集的详细工作流说明可参考文献[Zeegers 2022]。 <em>技术细节</em> 所有投影图像均为未经过多处理的原始文件，仅通过FleX-ray实验室软件进行了像素合并（binning）处理。处理后图像的分辨率为956×760。每份样本均附带平场校正图像（由10张前置与10张后置射线照片平均得到）与暗场校正图像（由10张前置与10张后置图像平均得到）。所有图像均以.tif格式存储。嵌入0~3颗碎石的样本数据包含于第1~4部分，而嵌入5~8颗碎石的样本数据构成第5部分。完整解压后的全部5部分数据集总容量约为343.5GB。用于目标检测任务、带有生成式真值标注的处理后数据集已通过另一项（容量更小的）提交包发布，链接为：https://zenodo.org/record/5681008 <strong>附加链接</strong><br> 本数据集由荷兰阿姆斯特丹的荷兰数学与计算机科学研究中心计算成像组（Computational Imaging group at Centrum Wiskunde & Informatica, CI-CWI）制作，官方页面链接：https://www.cwi.nl/research/groups/computational-imaging <strong>联系方式</strong><br> zeegers [at] cwi [dot] nl <strong>致谢</strong><br> 作者感谢荷兰科学研究组织（Netherlands Organisation for Scientific Research, NWO）的资助，项目编号为639.073.506。同时感谢TESCAN-XRE NV公司为FleX-ray实验室提供的协作与技术支持。 <br> <strong>参考文献</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, 《面向X射线异物检测的深度学习断层扫描工作流》，2022年（已投稿）<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, K. J. Batenburg，《探索性成像及其在FleX-ray实验室的实现》，《Journal of Imaging》，第6卷，第18期，2020年，DOI: 10.3390/jimaging6040018。 若您在出版物中使用本数据集（或其部分内容），我们将非常感谢您引用上述第一篇文献。
