5/11/2023 0 Comments Convert image format atlasFirst, images generated by these approaches are typically hundreds of gigabytes (GB) to terabytes (TB) in size, requiring efficient memory usage to view images on standard computers. Additional imaging modalities such as high resolution magnetic resonance imaging (MRI) ( Pallast et al., 2019) and electron microscopy (EM) ( Zheng et al., 2018) have further compounded the complexity and volume of 3D imaging requiring automated analysis.ģD microscopy poses several problems for standard image processing workflows. Previously, manual counts of cells and subcellular structures have been sufficient for 2D slices, but the advent of 3D microscopy tissue processing techniques such as serial two-photon tomography (STPT) (Kim et al., 2015 Ragan et al., 2012) and tissue clearing techniques (Mano et al., 2018) such as CLARITY (Chung et al., 2013), 3DISCO (Ertürk et al., 2012), and CUBIC (Susaki et al., 2014) combined with lightsheet microscopy (Hillman et al., 2019) for whole-organ imaging, generating thousands of slices per sample, has rendered manual approaches obsolete and required automated approaches to image processing and quantification. The growing number of large volume, high resolution 3D imaging datasets has required the concurrent development of new tools and pipelines to analyze this wealth of data in an efficient and verifiable manner ( Meijering et al., 2016 Renier et al., 2016). MagellanMapper leverages established open source computer vision libraries and is itself open source and freely available for download and extension. Using the command line interface, researchers can automate cell detection across volumetric images, refine anatomical atlas labels to fit underlying histology, register these atlases to sample images, and perform statistical analyses by anatomical region. At the microscopic level, researchers can inspect regions of interest at high resolution to build ground truth data of cellular locations such as nuclei positions. At the macroscopic level, the graphical interface allows researchers to view full volumetric images simultaneously in each dimension and to annotate anatomical label placements. To facilitate these analyses, MagellanMapper provides both a graphical user interface for manual inspection and a command line interface for automated image processing. The rapidly growing number of large volume, high resolution datasets necessitates visualization of raw data at both macro- and microscopic levels to assess the quality of data and automated processing to quantify data in an unbiased manner for comparison across a large number of samples. MagellanMapper is a software suite designed for visual inspection and end-to-end automated processing of large volume, 3D brain imaging datasets in a memory efficient manner.
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