BugNIST -- A New Large Scale Volumetric 3D Image Dataset for Classification and Detection

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.

Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems which motivate our approach originate from imaging commonly used in materials science and medicine. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation, and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.