Three-dimensional alignment and merging of confocal microscopy stacks

pre-printWe describe an efficient, robust, automated method for image alignment and merging of translated, rotated and flipped confocal microscopy stacks. The samples are captured in both directions (top and bottom) to increase the SNR of the individual slices. We identify the overlapping region of the two stacks by using a variable depth Maximum Intensity Projection (MIP) in the z dimension. For each depth tested, the MIP images gives an estimate of the angle of rotation between the stacks and the shifts in the x and y directions using the Fourier Shift property in 2D. We use the estimated rotation angle, shifts in the x and y direction and align the images in the z direction. A linear blending technique based on a sigmoidal function is used to maximize the information from the stacks and combine them. We get maximum information gain as we combine stacks obtained from both directions

Confocal Microscopy and Three-Dimensional Reconstruction of Thick, Transparent, Vital Tissue

The three-dimensional visualization of the 400 micron thick, transparent, in situ cornea is described to demonstrate the use of confocal light microscopy for noninvasive imaging of living cells and thick tissues in their normal, vital conditions. Specimen preparation and physiological stability, as well as light attenuation corrections are critical to data acquisition. The technique to provide mechanical stability of the specimen during the duration of the image acquisition is explained. A laser scanning confocal light microscope (LSCM) was used to obtain optical serial sections from rabbit eyes that were freshly removed and placed in a physiological Ringer\u27s solution. This study demonstrates the capability of the confocal light microscope to obtain a series of high contrast images, with a depth resolution of one micron, across the full thickness of living, transparent tissue. The problems of nonisotropic sampling and the limited eight-bit dynamic range are discussed. The three-dimensional reconstructions were obtained by computer graphics using the volume visualization projection technique. The three-dimensional visualization of the cornea in the in situ eye is presented as an example of image understanding of thick, viable biological cells and tissues. Finally, the criterion of image fidelity is explained. The techniques of confocal light microscopy with its enhanced lateral and axial resolution, improved image contrast, and volume visualization provides microscopists with new techniques for the observation of vital cells and tissues, both in vivo and in vitro

Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images

We introduce a method for large scale reconstruction of complex bundles of neural processes from fluorescent image stacks. We imaged yellow fluorescent protein labeled axons that innervated a whole muscle, as well as dendrites in cerebral cortex, in transgenic mice, at the diffraction limit with a confocal microscope. Each image stack was digitally re-sampled along an orientation such that the majority of axons appeared in cross-section. A region growing algorithm was implemented in the open-source Reconstruct software and applied to the semi-automatic tracing of individual axons in three dimensions. The progression of region growing is constrained by user-specified criteria based on pixel values and object sizes, and the user has full control over the segmentation process. A full montage of reconstructed axons was assembled from the ∼200 individually reconstructed stacks. Average reconstruction speed is ∼0.5 mm per hour. We found an error rate in the automatic tracing mode of ∼1 error per 250 um of axonal length. We demonstrated the capacity of the program by reconstructing the connectome of motor axons in a small mouse muscle

Reconstruction of the neuromuscular junction connectome

Motivation: Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets

Reconstructing neural circuits using multiresolution correlated light and electron microscopy

Correlated light and electron microscopy (CLEM) can be used to combine functional and molecular characterizations of neurons with detailed anatomical maps of their synaptic organization. Here we describe a multiresolution approach to CLEM (mrCLEM) that efficiently targets electron microscopy (EM) imaging to optically characterized cells while maintaining optimal tissue preparation for high-throughput EM reconstruction. This approach hinges on the ease with which arrays of sections collected on a solid substrate can be repeatedly imaged at different scales using scanning electron microscopy. We match this multiresolution EM imaging with multiresolution confocal mapping of the aldehyde-fixed tissue. Features visible in lower resolution EM correspond well to features visible in densely labeled optical maps of fixed tissue. Iterative feature matching, starting with gross anatomical correspondences and ending with subcellular structure, can then be used to target high-resolution EM image acquisition and annotation to cells of interest. To demonstrate this technique and range of images used to link live optical imaging to EM reconstructions, we provide a walkthrough of a mouse retinal light to EM experiment as well as some examples from mouse brain slices

Serial section registration of axonal confocal microscopy datasets for long-range neural circuit reconstruction

ManuscriptIn the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships amongst the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information