 | James Claude WarringtonThe Digital Imaging Group of London, London, ON, N6A 3K7, Canada | The digital imaging group of London , Department of Medical Imaging, Western University, London, ON, N6A ... |
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James Claude Warrington:Expert Impact
Concepts for whichJames Claude Warringtonhas direct influence:Neural foramina,Frontotemporal dementia,Direct estimation,Regional wall thicknesses,Ventricle lv,Learning network,Lv myocardium,Automated grading.
James Claude Warrington:KOL impact
Concepts related to the work of other authors for whichfor which James Claude Warrington has influence:Deep learning,Esophageal cancer,Ventricle segmentation,Frontotemporal dementia,Cervical spine,Convolutional neural network,Direct estimation.
KOL Resume for James Claude Warrington
| Year | |
|---|---|
| 2018 | The Digital Imaging Group of London, London, ON, N6A 3K7, Canada |
| 2017 | Digital Imaging Group, London, ON, Canada |
| 2016 | The Digital Imaging Group of London, Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada |
| Concept | World rank |
|---|---|
| nfs physicians | #3 |
| physicians nfs | #3 |
| annoying inefficiency | #3 |
| tasrl model | #3 |
| nf tasrl | #3 |
| accurate localization sbncuts | #3 |
| grading nfs | #3 |
| labels nf | #3 |
| learning tasrl | #3 |
| sbncuts nfs | #3 |
| sbncuts | #3 |
| tasrl incorporates | #3 |
| images task label | #3 |
| incorporates saliency | #3 |
| accurate grading nfs | #3 |
| images task labels | #3 |
| tasrl | #3 |
| nf object label | #3 |
| preserved intact structure | #3 |
| grading sbncuts | #3 |
| sbncuts efficient localization | #3 |
| direct spondylolisthesis | #4 |
| parameterized spine | #4 |
| spondylolisthesis identification | #4 |
| articulated parameterized | #4 |
| label nfs | #5 |
| nf candidates | #5 |
| physicians visual inspection | #5 |
| nf locations | #6 |
| thicknesses rwt | #6 |
| rwt lv dimensions | #8 |
| cardiac biventricle segmentation | #10 |
| areas rwt | #11 |
| temporal dynamic modeling | #11 |
| rwt dimensions | #11 |
| ventricle deep | #15 |
| incorporates task | #16 |
| sequences 145 | #17 |
| regression segmentation | #17 |
| classification nf | #17 |
| regional wall thicknesses | #17 |
| preliminary guess | #18 |
| severe ambiguities | #19 |
| common spinal disease | #21 |
| neural foraminal stenosis | #24 |
| localization grading | #25 |
| stenosis nfs | #25 |
| aware structural | #29 |
| quantification ventricle | #30 |
| nf class | #30 |
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Prominent publications by James Claude Warrington
Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness
[ PUBLICATION ]
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more challenging since the ...
| Known for Learning Network | Deep Multitask | Lv Indices | Quantification Ventricle | Cardiac Structure |
Using simultaneous PET/MRI to compare the accuracy of diagnosing frontotemporal dementia by arterial spin labelling MRI and FDG-PET
[ PUBLICATION ]
Purpose: The clinical utility of FDG-PET in diagnosing frontotemporal dementia (FTD) has been well demonstrated over the past decades. On the contrary, the diagnostic value of arterial spin labelling (ASL) MRI - a relatively new technique - in clinical diagnosis of FTD has yet to be confirmed. Using simultaneous PET/MRI, we evaluated the diagnostic performance of ASL in identifying pathological abnormalities in FTD (FTD) to determine whether ASL can provide similar diagnostic value as ...
| Known for Frontotemporal Dementia | Fdg Pet | Sensitivity Specificity | Arterial Spin | Magnetic Resonance |
Accurate estimation of regional wall thicknesses (RWT) of left ventricular (LV) myocardium from cardiac MR sequences is of significant importance for identification and diagnosis of cardiac disease. Existing RWT estimation still relies on segmentation of LV myocardium, which requires strong prior information and user interaction. No work has been devoted into direct estimation of RWT from cardiac MR images due to the diverse shapes and structures for various subjects and cardiac ...
| Known for Direct Estimation | Regional Wall Thicknesses | Lv Myocardium | Recurrent Neural | Cardiac Sequences |
Cardiac bi-ventricle segmentation can help physicians to obtain clinical indices, such as mass and volume of left ventricle (LV) and right ventricle (RV). In this paper, we propose a regression segmentation framework to delineate boundaries of bi-ventricle from cardiac magnetic resonance (MR) images by building a regression model automatically and accurately. First, we extract DAISY feature from images. Then, a point based representation method is employed to depict the boundaries. ...
| Known for Deep Regression | Ventricle Lv | Cardiac Images | Manual Segmentation | Model Based |
Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation
[ PUBLICATION ]
As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big ...
| Known for Neural Foramina | Boundary Delineation | Spinal Stenosis | Proposed Framework | Automated Accurate |
Automated grading of lumbar disc degeneration via supervised distance metric learning
[ PUBLICATION ]
| Known for Automated Grading | Lumbar Disc Degeneration |
Automated neural foraminal stenosis grading via task-aware structural representation learning
[ PUBLICATION ]
Neural foraminal stenosis (NFS) is the most common spinal disease in elderly patients, greatly affecting their quality of life. Efficient and accurate grading of NFS is extremely vital for physicians as it offers patients a timely and targeted treatment according to different grading levels. However, current clinical practice relies on physicians’ visual inspection and manual grading of neural foramina (NF), which brings the annoying inefficiency and inconsistency. A fully automated ...
| Known for Foraminal Stenosis | Spinal Disease | Slight Marked | Classification Normal |
Unsupervised discovery and extraction of common shapes from unlabeled images is a fundamental problem in object recognition and has broad applications in practice. However, shape discovery suffers from the lack of consistent matching methods for finding the correspondences between objects with different colors/textures among the input images. In this paper, we propose a novel unsupervised shape discovery method using Synchronized Spectral Network (SSN) which provides automatic part-part ...
