About Quantification of 3D Histological Data
3D histological data offers significant potential, but has proven challenging to quantify for much of the scientific community. Large terabyte-scale data sets produced by tissue clearing and lightsheet imaging require extensive computational resources. Open source solutions, such as ClearMap and BrainQuant3D (co-developed and developed by Translucence Co-Founder Ricardo Azevedo), are difficult to implement, requiring extensive coding experience not available to the average academic lab. To ease this pain point, through funding from the NIH BRAIN Initiative, we have developed the Translucence Teravoxel Toolkit (3TK) within VoxelsTM.
Translucence Biosystems AI-Powered Quantification
Our VoxelsTM software applies AI-powered workflows to detect, segment, and quantify objects of interest across entire samples. These workflows generate comprehensive insights into the density, size, distribution, intensity, and volume metrics of objects of interest.
VoxelsTM leverages cloud cluster computing and a user-friendly job runner to streamline and simplify the process of 3D-data quantification. This, in combination with our custom AI-powered segmentation workflows, allows for the rapid generation of actionable datasets with unparalleled accuracy and insights into systems-level biology. In the case of mouse brain samples, we employ the Allen Brain Atlas and custom warping algorithms to provide comprehensive whole-brain anatomical and regional quantification. These quantification techniques are perfectly suited for investigating behavioral, genetic, and pharmacological manipulations in a wide variety of sample types.
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Summary
At Translucence Biosystems we employ our VoxelsTM software to segment objects across whole samples. For brain samples, we are able to warp those samples to the Allen reference atlas to generate robust and unbiased data across hundreds of brain regions.
