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.

A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network

The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a statistical correspondence between the reconstructed data and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.