What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Magnetic resonance imaging
His scientific interests lie mostly in Artificial intelligence, Computer vision, Segmentation, Magnetic resonance imaging and White matter.
His research integrates issues of Machine learning, Metric and Pattern recognition in his study of Artificial intelligence.
In most of his Computer vision studies, his work intersects topics such as Visualization.
Many of his research projects under Segmentation are closely connected to Line filter with Line filter, tying the diverse disciplines of science together.
His Scale-space segmentation research integrates issues from Reliability and Surgical planning.
His White matter study incorporates themes from Audiology, Diffusion MRI, Pathology, Autism and Neuroscience.
His most cited work include:
- User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (4164 citations)
- Nonlinear anisotropic filtering of MRI data (1041 citations)
- Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. (914 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Segmentation, Pattern recognition and White matter are his primary areas of study.
As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Neuroimaging and, on occasion, Autism.
He has included themes like Algorithm, Visualization and Atlas in his Computer vision study.
His biological study spans a wide range of topics, including Brain atlas, Medical imaging and Image processing.
His White matter research includes elements of Diffusion MRI, Neuroscience, Brain development and Brain size.
In his research, Splenium is intimately related to Corpus callosum, which falls under the overarching field of Diffusion MRI.
He most often published in these fields:
- Artificial intelligence (46.59%)
- Computer vision (28.88%)
- Segmentation (22.89%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (46.59%)
- Pattern recognition (21.53%)
- Computer vision (28.88%)
In recent papers he was focusing on the following fields of study:
Guido Gerig mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Autism spectrum disorder.
His Artificial intelligence research includes themes of Machine learning and Diffeomorphism.
His Pattern recognition research is multidisciplinary, incorporating elements of Topology, Deep learning, Geodesic and Regression.
His Computer vision study deals with Optical coherence tomography intersecting with Glaucoma and Optic nerve.
His study of Image segmentation is a part of Segmentation.
His work on Active contour model is typically connected to Context and Track disease as part of general Image segmentation study, connecting several disciplines of science.
Between 2015 and 2021, his most popular works were:
- Early brain development in infants at high risk for autism spectrum disorder (461 citations)
- Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age (143 citations)
- Increased Extra-axial Cerebrospinal Fluid in High-Risk Infants Who Later Develop Autism (104 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Internal medicine
Guido Gerig mainly investigates Autism spectrum disorder, Audiology, Neuroimaging, Autism and Default mode network.
Guido Gerig has researched Autism spectrum disorder in several fields, including White matter, Theory of mind, Cognition, Neuroscience and Empathy.
In his work, Guido Gerig performs multidisciplinary research in White matter and Association.
Guido Gerig combines subjects such as Developmental psychology, Fractional anisotropy and Corpus callosum with his study of Audiology.
The Neuroimaging study combines topics in areas such as Neurodevelopmental disorder, Pediatrics, First year of life and Subarachnoid space.
His Autism study integrates concerns from other disciplines, such as Familial risk and Magnetic resonance imaging.
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