Alessandro Bria

Confocal light sheet microscopy: micron-scale neuroanatomy of the entire mouse brain

Elucidating the neural pathways that underlie brain function is one of the greatest challenges in neuroscience. Light sheet based microscopy is a cutting edge method to map cerebral circuitry through optical sectioning of cleared mouse brains. However, the image contrast provided by this method is not sufficient to resolve and reconstruct the entire neuronal network. Here we combined the advantages of light sheet illumination and confocal slit detection to increase the image contrast in real time, with a frame rate of 10 Hz. In fact, in confocal light sheet microscopy (CLSM), the out-of-focus and scattered light is filtered out before detection, without multiple acquisitions or any post-processing of the acquired data. The background rejection capabilities of CLSM were validated in cleared mouse brains by comparison with a structured illumination approach. We show that CLSM allows reconstructing macroscopic brain volumes with sub-cellular resolution. We obtained a comprehensive map of Purkinje cells in the cerebellum of L7-GFP transgenic mice. Further, we were able to trace neuronal projections across brain of thy1-GFP-M transgenic mice. The whole-brain high-resolution fluorescence imaging assured by CLSM may represent a powerful tool to navigate the brain through neuronal pathways. Although this work is focused on brain imaging, the macro-scale high-resolution tomographies affordable with CLSM are ideally suited to explore, at micron-scale resolution, the anatomy of different specimens like murine organs, embryos or flies.

Extensible visualization and analysis for multidimensional images using Vaa3D

Open-Source 3D Visualization-Assisted Analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. Once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.

Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

Learning from unbalanced data: A cascade-based approach for detecting clustered microcalcifications

Finding abnormalities in diagnostic images is a difficult task even for expert radiologists because the normal tissue locations largely outnumber those with suspicious signs which may thus be missed or incorrectly interpreted. For the same reason the design of a Computer-Aided Detection (CADe) system is very complex because the large predominance of normal samples in the training data may hamper the ability of the classifier to recognize the abnormalities on the images. In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error. Such approach is used to design a system (CasCADe) for the automated detection of clustered microcalcifications (μCs), which is a severely unbalanced classification problem because of the vast majority of image locations where no μC is present. The proposed approach was evaluated with a dataset of 1599 full-field digital mammograms from 560 cases and compared favorably with the Hologic R2CAD ImageChecker, one of the most widespread commercial CADe systems. In particular, at the same lesion sensitivity of R2CAD (90%) on biopsy proven malignant cases, CasCADe and R2CAD detected 0.13 and 0.21 false positives per image (FPpi), respectively (p-value=0.09), whereas at the same FPpi of R2CAD (0.21), CasCADe and R2CAD detected 93% and 90% of true lesions respectively (p-value=0.11) thus showing that CasCADe can compete with high-end CADe commercial systems.

TeraStitcher - A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images

BackgroundFurther advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data.ResultsWe propose a free and fully automated 3D Stitching tool designed to match the special requirements coming out of teravoxel-sized tiled microscopy images that is able to stitch them in a reasonable time even on workstations with limited resources. The tool was tested on teravoxel-sized whole mouse brain images with micrometer resolution and it was also compared with the state-of-the-art stitching tools on megavoxel-sized publicy available datasets. This comparison confirmed that the solutions we adopted are suited for stitching very large images and also perform well on datasets with different characteristics. Indeed, some of the algorithms embedded in other stitching tools could be easily integrated in our framework if they turned out to be more effective on other classes of images. To this purpose, we designed a software architecture which separates the strategies that use efficiently memory resources from the algorithms which may depend on the characteristics of the acquired images.ConclusionsTeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (<8 GB) and processing power. It exploits the knowledge of approximate tile positions and uses ad-hoc strategies and algorithms designed for such very large datasets. The produced images can be saved into a multiresolution representation to be efficiently retrieved and processed. We provide TeraStitcher both as standalone application and as plugin of the free software Vaa3D.

An effective learning strategy for cascaded object detection

Object detection is frequently a complex, severely unbalanced classification problem.A cascade of node classifiers allows us to efficiently handle the complexity.In our proposal, each node classifier is trained with a ranking-based algorithm.Ranking effectively faces the imbalance between object and non-object patches.Our method is effective if compared to other learning strategies for skewed classes. To distinguish objects from non-objects in images under computational constraints, a suitable solution is to employ a cascade detector that consists of a sequence of node classifiers with increasing discriminative power. However, among the millions of image patches generated from an input image, only very few contain the searched object. When trained on these highly unbalanced data sets, the node classifiers tend to have poor performance on the minority class. Thus, we propose a learning strategy aimed at maximizing the node classifiers ranking capability rather than their accuracy. We also provide an efficient implementation yielding the same time complexity of the original Viola-Jones cascade training. Experimental results on highly unbalanced real problems show that our approach is both efficient and effective when compared to other node training strategies for skewed classes.

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.

TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images

networks, coexpression to rescue RNA interference– or CRISPRCAS9–induced reduction of endogenous transcripts, and expression of ORFs carrying a mutation of interest to allow measurement of the mutation effect in the absence of the wild-type background. High-level gene coverage, combined with the versatility of Gateway cloning, and full access to OC clones make this collection a unique and valuable resource for the scientific community that should aid in the functional characterization of new protein targets and testing of disease-relevant mutations on a large scale. The OC resource will continue to be expanded in the future to increase human gene coverage, provide additional isoforms where available, provide clones with medically relevant mutations and add additional species, including ORFs from Xenopus and Drosophila.

An open-source VAA3D plugin for real-time 3D visualization of terabyte-sized volumetric images

Modern high-throughput bioimaging techniques pose the unprecedented challenge of exploring and analyzing the produced Terabyte-scale volumetric images directly in their 3D space. Without expensive virtual reality devices and/or parallel computing infrastructures, this becomes even more demanding and calls for new, more scalable tools that help exploring these very large 3D data also on common laptops and graphic hardware. To this end, we developed a plugin for the open-source, cross-platform Vaa3D system to extend its powerful 3D visualization and analysis capabilities to images of potentially unlimited size. When used with large volumetric images up to 2.5 Terabyte in size, Vaa3D-TeraFly exhibited real-time (subsecond) performance that scaled constantly on image size. The tool has been implemented in C++ with Qt and OpenGL and it is freely and publicly available both as open-source and as binary package along with the main Vaa3D distribution.

Label-free near-infrared reflectance microscopy as a complimentary tool for two-photon fluorescence brain imaging

In vivo two-photon imaging combined with targeted fluorescent indicators is currently extensively used for attaining critical insights into brain functionality and structural plasticity. Additional information might be gained from back-scattered photons from the near-infrared (NIR) laser without introducing any exogenous labelling. Here, we describe a complimentary and versatile approach that, by collecting the reflected NIR light, provides structural details on axons and blood vessels in the brain, both in fixed samples and in live animals under a cranial window. Indeed, by combining NIR reflectance and two-photon imaging of a slice of hippocampus from a Thy1-GFPm mouse, we show the presence of randomly oriented axons intermingled with sparsely fluorescent neuronal processes. The back-scattered photons guide the contextualization of the fluorescence structure within brain atlas thanks to the recognition of characteristic hippocampal structures. Interestingly, NIR reflectance microscopy allowed the label-free detection of axonal elongations over the superficial layers of mouse cortex under a cranial window in vivo. Finally, blood flow can be measured in live preparations, thus validating label free NIR reflectance as a tool for monitoring hemodynamic fluctuations. The prospective versatility of this label-free technique complimentary to two-photon fluorescence microscopy is demonstrated in a mouse model of photothrombotic stroke in which the axonal degeneration and blood flow remodeling can be investigated.