What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Internal medicine
- Cancer
Elizabeth Bullitt mainly investigates Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Noxious stimulus.
Her Artificial intelligence research incorporates themes from Tree, Ridge, Tissue surface and Metric.
Her Computer vision research is multidisciplinary, incorporating elements of Tree traversal and Monte Carlo method.
Her Segmentation research includes elements of Brain atlas, Visualization and Surgical planning.
Her Pattern recognition study combines topics from a wide range of disciplines, such as Regression analysis and Contrast.
Her work deals with themes such as Region of interest, Radiology and Magnetic resonance angiography, which intersect with Image segmentation.
Her most cited work include:
- Expression of C-fos-like protein as a marker for neuronal activity following noxious stimulation in the rat (819 citations)
- Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction (545 citations)
- A brain tumor segmentation framework based on outlier detection. (465 citations)
What are the main themes of her work throughout her whole career to date?
Elizabeth Bullitt mostly deals with Artificial intelligence, Computer vision, Radiology, Segmentation and Magnetic resonance imaging.
Her research on Artificial intelligence often connects related topics like Pattern recognition.
Her Computer vision course of study focuses on Monte Carlo method and Tree traversal.
Her study on Magnetic resonance angiography, Medical imaging and Portal vein is often connected to Tortuosity as part of broader study in Radiology.
Her Scale-space segmentation study, which is part of a larger body of work in Segmentation, is frequently linked to Expectation–maximization algorithm, bridging the gap between disciplines.
In her research on the topic of Magnetic resonance imaging, Image warping and Central nervous system is strongly related with Anatomy.
She most often published in these fields:
- Artificial intelligence (41.23%)
- Computer vision (28.95%)
- Radiology (19.30%)
What were the highlights of her more recent work (between 2008-2014)?
- Artificial intelligence (41.23%)
- Data set (7.02%)
- Principal component analysis (6.14%)
In recent papers she was focusing on the following fields of study:
Elizabeth Bullitt focuses on Artificial intelligence, Data set, Principal component analysis, Tree and Magnetic resonance imaging.
The various areas that Elizabeth Bullitt examines in her Artificial intelligence study include White matter, Machine learning, Logistic regression, Diffusion MRI and Pattern recognition.
Her White matter study combines topics in areas such as Dynamic contrast-enhanced MRI, Real-time MRI, Pathology, Gold standard and Computer vision.
Her Data set research focuses on subjects like Medical imaging, which are linked to Medical physics, Biopsy and Brain tumor.
Her research on Tree also deals with topics like
- Structure that connect with fields like Variation,
- Representation which connect with Visualization.
Her study of Magnetic resonance angiography is a part of Magnetic resonance imaging.
Between 2008 and 2014, her most popular works were:
- Simulation of brain tumors in MR images for evaluation of segmentation efficacy. (102 citations)
- The Effect of Exercise on the Cerebral Vasculature of Healthy Aged Subjects as Visualized by MR Angiography (100 citations)
- White matter abnormalities revealed by diffusion tensor imaging in non-demented and demented HIV+ patients (99 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Statistics
Her main research concerns Internal medicine, White matter, Diffusion MRI, Magnetic resonance imaging and Fractional anisotropy.
Her study in the field of Breast cancer and Bevacizumab is also linked to topics like Carboplatin and In patient.
Her biological study spans a wide range of topics, including Region of interest, Voxel and Corpus callosum, Pathology.
Her studies examine the connections between Diffusion MRI and genetics, as well as such issues in Gold standard, with regards to Artificial intelligence.
Her studies in Artificial intelligence integrate themes in fields like Biomedical engineering and Computer vision.
She combines subjects such as Blood vessel, Circulatory system, Angiography and Anatomy with her study of Magnetic resonance imaging.
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