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Structural MRI images Human Macroscopic MRI datasets Healthy and Alzheimer's disease: Yes [4] Big Brain 3D reconstruction of complete brain from cell-body stained histology sections at 20 micron isotropic resolution Human Microscopic Images Healthy No [5] BIRN fMRI and MRI data fMRI, MRI scans and atlases for human and mouse brains Mouse, Human
The FMRIB Software Library, abbreviated FSL, is a software library containing image analysis and statistical tools for functional, structural and diffusion MRI brain imaging data. FSL is available as both precompiled binaries and source code for Apple and PC computers. It is freely available for non-commercial use.
Neuroimaging software is used to study the structure and function of the brain. To see an NIH Blueprint for Neuroscience Research funded clearinghouse of many of these software applications, as well as hardware, etc. go to the NITRC web site. 3D Slicer Extensible, free open source multi-purpose software for visualization and analysis.
Photorealistic retinal images and vessel segmentations. Public domain. 2500 images with 1500*1152 pixels useful for segmentation and classification of veins and arteries on a single background. 2500 Images Classification, Segmentation 2020 [261] C. Valenti et al. EEG Database Study to examine EEG correlates of genetic predisposition to alcoholism.
The Allen Brain Atlas lets researchers view the areas of differing expression in the brain which enables the viewing of neural connections throughout the brain. Viewing these pathways through differing gene expression as well as functional imaging techniques permits researchers to correlate between gene expression, cell types, and pathway ...
The first MR images of a human brain were obtained in 1978 by two groups of researchers at EMI Laboratories led by Ian Robert Young and Hugh Clow. [1] In 1986, Charles L. Dumoulin and Howard R. Hart at General Electric developed MR angiography, [2] and Denis Le Bihan obtained the first images and later patented diffusion MRI. [3]
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Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.