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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.
Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. Although there are many computer vision techniques for image segmentation, some have been adapted specifically for medical image computing. Below is a sampling of techniques within this field; the implementation relies on the expertise that ...
A number of online neuroscience databases are available which provide information regarding gene expression, neurons, macroscopic brain structure, and neurological or psychiatric disorders. Some databases contain descriptive and numerical data, some to brain function, others offer access to 'raw' imaging data, such as postmortem brain sections ...
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to generate pictures of the anatomy and the physiological processes inside the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to form images of the organs in the body.
The first study of the human brain at 3.0 T was published in 1994, [13] and in 1998 at 8 T. [14] Studies of the human brain have been performed at 9.4 T (2006) [15] and up to 10.5 T (2019). [16] Paul Lauterbur and Sir Peter Mansfield were awarded the 2003 Nobel Prize in Physiology or Medicine for their discoveries concerning MRI.
Second, brain image data can be acquired using different neuroimaging modalities. Third, brain properties can be analyzed at different scales (e.g. in the whole brain, regions of interest, cortical or subcortical structures). Fourth, the data can be subjected to different kinds of processing and analysis steps.
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
Multispectral segmentation is a method for differentiating tissue classes of similar characteristics in a single imaging modality using several independent images of the same anatomical slice in different modalities (e.g., T2, proton density, T1, etc.).