A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography

RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations

Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.

Quantification of Tumor Vasculature by Analysis of Amount and Spatial Dispersion of Caliber-Classified Vessels

This protocol focuses on the quantitative description of the angioarchitecture of experimental tumor xenografts. This semiautomatic analysis is carried out on functional vessels and microvessels acquired by confocal imaging and processed into progressively reconstructed angioarchitectures following a caliber-classification step. The protocol can be applied also to the quantification of pathological angioarchitectures other than tumor grafts as well as to the microvasculature of physiological tissue samples.

Investigation of inner ear anatomy in mouse using X-ray phase contrast tomography

A thorough understanding of inner ear anatomy is important for investigators. However, investigation of the mouse inner ear is difficult due to the limitations of imaging techniques. X-ray phase contrast tomography increases contrast 100-1,000 times compared with conventional X-ray imaging. This study aimed to investigate inner ear anatomy in a fresh post-mortem mouse using X-ray phase contrast tomography and to provide a comprehensive atlas of microstructures with less tissue deformation. All experiments were performed in accordance with our institution's guidelines on the care and use of laboratory animals. A fresh mouse cadaver was scanned immediately after sacrifice using an inline phase contrast tomography system. Slice images were reconstructed using a filtered back-projection (FBP) algorithm. Standardized axial and coronal planes were adjusted with a multi-planar reconstruction method. Some three-dimensional (3D) objects were reconstructed by surface rendering. The characteristic features of microstructures, including otoconia masses of the saccular and utricular maculae, superior and inferior macula cribrosae, single canal, modiolus, and osseous spiral lamina, were described in detail. Spatial positions and relationships of the vestibular structures were exhibited in 3D views. This study investigated mouse inner ear anatomy and provided a standardized presentation of microstructures. In particular, otoconia masses were visualized in their natural status without contrast for the first time. The comprehensive anatomy atlas presented in this study provides an excellent reference for morphology studies of the inner ear.

Vascular amounts and dispersion of caliber-classified vessels as key parameters to quantitate 3D micro-angioarchitectures in multiple myeloma experimental tumors

Blood vessel micro-angioarchitecture plays a pivotal role in tumor progression, metastatic dissemination and response to therapy. Thus, methods able to quantify microvascular trees and their anomalies may allow a better comprehension of the neovascularization process and evaluation of vascular-targeted therapies in cancer. To this aim, the development of a restricted set of indexes able to describe the arrangement of a microvascular tree is eagerly required. We addressed this goal through 3D analysis of the functional microvascular network in sulfo-biotin-stained human multiple myeloma KMS-11 xenografts in NOD/SCID mice. Using image analysis, we show that amounts, spatial dispersion and spatial relationships of adjacent classes of caliber-filtered microvessels provide a near-linear graphical “fingerprint” of tumor micro-angioarchitecture. Position, slope and axial projections of this graphical outcome reflect biological features and summarize the properties of tumor micro-angioarchitecture. Notably, treatment of KMS-11 xenografts with anti-angiogenic drugs affected position and slope of the specific curves without degrading their near-linear properties. The possibility offered by this procedure to describe and quantify the 3D features of the tumor micro-angioarchitecture paves the way to the analysis of the microvascular tree in human tumor specimens at different stages of tumor progression and after pharmacologic interventions, with possible diagnostic and prognostic implications.