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

Abstract

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.

Exploration and visualization of connectivity in the adult mouse brain

“…Beyond the present contribution the field of segmentation and visualization of neuronal connectivity is well developed with many interesting and successful approaches and is a major charge of a growing bioimage informatics community [18]. In particular, specialized volumetric image segmentation techniques such as the open source Vaa3D [19] have been developed for rapid annotation of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human, and reconstructing images of 1107 Drosophila GAL4 lines forming a projectome of a Drosophila brain [20]. Tools such as these and the commercial based Amira (http://www.fei.com/software/amira-3d-for-life-sciences/) can be used to extend or complement techniques here.…”

Fine and distributed subcellular retinotopy of excitatory inputs to the dendritic tree of a collision-detecting neuron

“…COMs were computed after pooling suprathreshold pixels across trials. To compute the dendritic distance between every two dendritic branches activated above threshold in response to a single-facet stimulus, we first performed a three-dimensional reconstruction of the two-photon image stacks of the whole LGMD excitatory dendrite in six animals and marked the shortest dendritic path connecting the centers of the activated regions on each pair of dendritic branches using Vaa3D (Peng et al 2010(Peng et al , 2014a(Peng et al , 2014b. The marked dendritic paths were loaded to MATLAB, and the dendritic distances between each pair of activated branches were computed.…”

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

“…Neuron_Morpho 43 and Neurolucida 44 ) enabled the manual delineation of neurites in a single plane. Although more recent methods allow the segmentation of neuronal processes in 3D by delineating 2D projected images 45 , manual annotation is slow and labour-intensive, and therefore not easily amenable to upscaling. Although the nomenclature and classification of automated neuron tracing algorithms is not consistent in literature, from an image informatics perspective, we discern global image processing methods, local tracing methods, and algorithms that use a combination of both.…”