What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Magnetic resonance imaging
Artificial intelligence, Computer vision, Magnetic resonance imaging, Image registration and Segmentation are his primary areas of study.
His studies deal with areas such as Topology, Surface and Pattern recognition as well as Artificial intelligence.
His research in the fields of Image processing, Canny edge detector and Three-dimensional space overlaps with other disciplines such as Volume.
His Magnetic resonance imaging research incorporates elements of Image resolution, Atlas, Anatomy and Brain mapping.
Grégoire Malandain has researched Image registration in several fields, including Matching, Similarity measure, Robustness and Computed tomography.
His Segmentation research is multidisciplinary, incorporating perspectives in Anatomy & histology, Medical imaging, Medical physics, Tomography and Topological property.
His most cited work include:
- Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques (827 citations)
- The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration (464 citations)
- Model-Based Detection of Tubular Structures in 3D Images (368 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Image processing.
His Artificial intelligence research is multidisciplinary, relying on both Tomography and Affine transformation.
His research integrates issues of Imaging phantom, Robustness and Medical imaging in his study of Computer vision.
His Segmentation study also includes fields such as
- Atlas and related Magnetic resonance imaging,
- Atlas which connect with Head and neck and Medical physics.
His Image processing research includes elements of Algorithm and Histogram.
Grégoire Malandain regularly ties together related areas like Similarity measure in his Image registration studies.
He most often published in these fields:
- Artificial intelligence (47.50%)
- Computer vision (38.33%)
- Segmentation (15.42%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (47.50%)
- Computer vision (38.33%)
- Pattern recognition (12.08%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Arabidopsis.
His work on Voxel as part of general Artificial intelligence study is frequently linked to Standard deviation, therefore connecting diverse disciplines of science.
His research brings together the fields of Sequence and Computer vision.
Grégoire Malandain combines subjects such as Machine learning and Spatial normalization with his study of Pattern recognition.
His Image segmentation research incorporates elements of Hough transform, Dendrite and Interpolation.
His research on Arabidopsis also deals with topics like
- 3d microscopy together with Botany, Arabidopsis thaliana and Process,
- Regulation of gene expression that connect with fields like Morphogenesis, Multicellular organism, Mutant, Regulator and Transcription factor.
Between 2011 and 2021, his most popular works were:
- In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level (177 citations)
- Dentalmaps: automatic dental delineation for radiotherapy planning in head-and-neck cancer. (21 citations)
- A tree-topology preserving pairing for 3D/2D registration (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Grégoire Malandain mainly focuses on Artificial intelligence, Computer vision, Embryogenesis, Pattern recognition and Light sheet fluorescence microscopy.
He has researched Artificial intelligence in several fields, including Sperm, Semen quality and Sperm quality.
The study incorporates disciplines such as Flagellum and Sperm motility, Semen in addition to Computer vision.
The various areas that he examines in his Embryogenesis study include Embryonic stem cell and Cell.
His studies deal with areas such as Voxel, Spatial normalization, Nuclear medicine and Statistical parametric mapping as well as Pattern recognition.
Grégoire Malandain has included themes like Positron emission tomography, Linear model and Correlation in his Voxel study.
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