Progress in 3D volumetric image analysis research is limited by the lack of datasets and most advances in analysis methods for volumetric images are based on medical data. However, medical data do not necessarily resemble the characteristics of other volumetric images such as micro-CT. To promote research in 3D volumetric image analysis beyond medical data, we have created the BugNIST dataset and made it freely available. BugNIST is an extensive dataset of micro-CT scans of 12 types of bugs, such as insects and larvae. BugNIST contains 9437 volumes where 9087 are of individual bugs and 350 are mixtures of bugs and other material. The goal of BugNIST is to benchmark classification and detection methods, and we have designed the detection challenge such that detection models are trained on scans of individual bugs and tested on bug mixtures. Models capable of solving this task will be independent of the context, i.e., the surrounding material. This is a great advantage if the context is unknown or changing, as is often the case in micro-CT. Our initial baseline analysis shows that current state-of-the-art deep learning methods classify individual bugs very well, but has great difficulty with the detection challenge. Hereby, BugNIST enables research in image analysis areas that until now have missed relevant data - both classification, detection, and hopefully more.

翻译：三维体积图像分析研究的进展受限于数据集的匮乏，目前体积图像分析方法的大部分突破均基于医学数据。然而，医学数据未必能反映其他体积图像（如显微CT）的特征。为促进医学数据之外的三维体积图像分析研究，我们创建了BugNIST数据集并免费开放。BugNIST是一个包含12类虫体（如昆虫与幼虫）显微CT扫描影像的大型数据集，共收录9437个体积数据，其中9087个为单虫体样本，350个为虫体与其他材料的混合物。该数据集旨在为分类与检测方法提供基准测试，我们设计的检测任务要求模型基于单虫体扫描数据训练，并在虫体混合物中完成测试。能够解决该任务的模型将具备场景无关性，即不受周围材料的干扰——这在显微CT应用场景中（常面临未知或变化的背景环境）具有显著优势。初步基线分析表明，当前最先进的深度学习方法虽能精准实现单虫体分类，但在检测任务中仍面临重大挑战。由此，BugNIST为图像分析领域（涵盖分类、检测乃至更广义的研究方向）提供了此前缺失的关键数据支持。