Abstract

With recent technological advances in microscopy and image acquisition of tissue sections, further developments of tools are required for viewing, transforming, and analyzing the ever-increasing amounts of high-resolution data produced. In the field of neuroscience, histological images of whole rodent brain sections are commonly used for investigating brain connections as well as cellular and molecular organization in the normal and diseased brain, but present a problem for the typical neuroscientist with no or limited programming experience in terms of the pre- and post-processing steps needed for analysis. To meet this need we have designed Nutil, an open access and stand-alone executable software that enables automated transformations, post-processing, and analyses of 2D section images using multi-core processing (OpenMP). The software is written in C++ for efficiency, and provides the user with a clean and easy graphical user interface for specifying the input and output parameters. Nutil currently contains four separate tools: (1) A transformation toolchain named “Transform” that allows for rotation, mirroring and scaling, resizing, and renaming of very large tiled tiff images. (2) “TiffCreator” enables the generation of tiled TIFF images from other image formats such as PNG and JPEG. (3) A “Resize” tool completes the preprocessing toolset and allows downscaling of PNG and JPEG images with output in PNG format. (4) The fourth tool is a post-processing method called “Quantifier” that enables the quantification of segmented objects in the context of regions defined by brain atlas maps generated with the QuickNII software based on a 3D reference atlas (mouse or rat). The output consists of a set of report files, point cloud coordinate files for visualization in reference atlas space, and reference atlas images superimposed with color-coded objects. The Nutil software is made available by the Human Brain Project (https://www.humanbrainproject.eu) at https://www.nitrc.org/projects/nutil/.

Highlights

  • The process of changing data from one “raw” format to another to make the data suitable for analysis – often referred to as data wrangling or data transformation – is generally required when employing standardized analytical pipelines

  • Nutil can be used independently of the QUINT workflow to preprocess images in preparation for other downstream processes. It enables automated transformations such as rotation and scaling, cropping, resizing, and renaming; in addition to analytical post-processing of segmentations that are generated from the brain section images, based on input from customized reference atlas maps (Figure 1)

  • The output results are assembled based on information provided in the Nutil graphical user interface (GUI), which includes the option to define customized regions together with a name (“Cortex”) and color (“red”)

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Summary

INTRODUCTION

The process of changing data from one “raw” format to another to make the data suitable for analysis – often referred to as data wrangling or data transformation – is generally required when employing standardized analytical pipelines. Nutil can be used independently of the QUINT workflow to preprocess images in preparation for other downstream processes It enables automated transformations such as rotation and scaling, cropping, resizing, and renaming; in addition to analytical post-processing of segmentations that are generated from the brain section images, based on input from customized reference atlas maps (Figure 1). In order to solve the memory problem, Nutil Transform (rotate, flipping, and renaming) operates on tiled TIFF files only, with a separate TiffCreator tool included for converting JPEG/PNG images to the required tiled TIFF format. The output results are assembled based on information provided in the Nutil GUI, which includes the option to define customized regions (ensembles of reference brain region IDs) together with a name (“Cortex”) and color (“red”) (via an Excel template; see Supplementary File 2) This enables the user to constrain the output data to specific areas of the brain (e.g., “Amygdala,”, “Hippocampus,” “Primary Somatosensory Cortex”). When all threads have completed, combine areas into one object and calculate both individual slice and global statistics

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