A comprehensive pipeline for analyzing functional MRI data, including preprocessing, statistical modeling with GLM, and multiple comparison correction. It provides both GUI and command-line interfaces for setting up and running complete fMRI analyses.
A modern, powerful 3D/4D image viewer built on Python and OpenGL that replaces the older FSLView. It supports multi-panel layouts, surface rendering, and extensive overlay options for visualizing complex neuroimaging data.
A robust algorithm for skull-stripping (removing non-brain tissue) from structural MRI images using a deformable model approach with optional bias field correction and eye/optic nerve removal.
A suite of tools for analyzing diffusion-weighted MRI data, including eddy current correction, tensor fitting, probabilistic tractography, and bedpostx for crossing fiber modeling.
A tool for performing Independent Component Analysis (ICA) on fMRI data to identify spatially independent patterns of brain activity without requiring a pre-specified model.
A model-based segmentation and registration tool that uses shape and appearance models to segment subcortical structures from T1-weighted MRI images with high accuracy.
Researchers use FSL to analyze structural and functional MRI data from patients with neurological disorders (e.g., Alzheimer's, Parkinson's, multiple sclerosis) to identify biomarkers, track disease progression, and evaluate treatment effects. By comparing patient groups to healthy controls using FSL's statistical tools, researchers can detect patterns of brain atrophy, functional connectivity changes, or white matter abnormalities that correlate with clinical symptoms or predict outcomes.
Cognitive neuroscientists employ FSL's FEAT pipeline to analyze task-based fMRI data from experiments studying perception, memory, decision-making, or emotion. They design GLM models with appropriate regressors for different experimental conditions, use cluster correction for multiple comparisons, and visualize resulting activation maps to identify brain regions involved in specific cognitive processes, often combining results across subjects in group-level analyses.
Researchers use MELODIC for ICA-based analysis of resting-state fMRI data to identify intrinsic brain networks (like the default mode network) and study how their connectivity is altered in psychiatric conditions (depression, schizophrenia) or by interventions (medication, therapy). FSL's network-based statistics tools then allow comparison of connectivity patterns between groups, revealing network-level disruptions associated with mental health disorders.
Neuroscientists and clinicians utilize FSL's FDT toolbox to analyze diffusion MRI data for studying white matter integrity and connectivity. They perform tractography to reconstruct specific white matter pathways (like the arcuate fasciculus or corticospinal tract), measure diffusion metrics (FA, MD) along these tracts, and correlate them with behavioral measures or clinical outcomes in conditions like traumatic brain injury, stroke, or developmental disorders.
Advanced researchers combine multiple imaging modalities using FSL's registration and fusion capabilities. They might register fMRI activation maps to high-resolution structural images, overlay diffusion tractography on functional networks, or combine PET metabolic data with MRI anatomy. FSL's flexible registration tools (FLIRT, FNIRT) enable precise alignment between different modalities and template spaces, facilitating comprehensive multi-modal analyses.
Universities and training programs use FSL as a primary teaching tool in neuroimaging courses due to its comprehensive documentation, example datasets, and mix of GUI and command-line interfaces. Students learn fundamental concepts through hands-on exercises with real data, progressing from basic preprocessing to advanced statistical analysis, preparing them for research careers in brain imaging.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
