 | Xiaoxu HeThe digital imaging group of London , Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada | Department of Medical Imaging, University of Western ... |
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Xiaoxu He:Expert Impact
Concepts for whichXiaoxu Hehas direct influence:Neural foramina,Neural foraminal stenosis,Automated grading,Lumbar disc degeneration,Boundary delineation,Accurate grading nfs,Task labels,Automated neural.
Xiaoxu He:KOL impact
Concepts related to the work of other authors for whichfor which Xiaoxu He has influence:Cervical spine,Semantic segmentation,Manual annotation,Heart image,Intervertebral discs,Multiple spinal structures,Anteroposterior diameter.
KOL Resume for Xiaoxu He
| Year | |
|---|---|
| 2018 | The digital imaging group of London , Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada |
| 2017 | Department of Medical Imaging, University of Western Ontario, London, ON, Canada N6A 3K7 Western Univ. (Canada) Digital Imaging Group (DIG), London, ON, Canada N6A 4V2 |
| 2016 | The Digital Imaging Group of London, Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada |
| Concept | World rank |
|---|---|
| images task labels | #4 |
| tasrl | #4 |
| nf tasrl | #4 |
| tasrl incorporates | #4 |
| images task label | #4 |
| grading sbncuts | #4 |
| sbncuts | #4 |
| sbncuts nfs | #4 |
| nf object label | #4 |
| annoying inefficiency | #4 |
| preserved intact structure | #4 |
| nfs physicians | #4 |
| labels nf | #4 |
| incorporates saliency | #4 |
| accurate grading nfs | #4 |
| grading nfs | #4 |
| physicians nfs | #4 |
| accurate localization sbncuts | #4 |
| tasrl model | #4 |
| learning tasrl | #4 |
| sbncuts efficient localization | #4 |
| nf candidates | #6 |
| physicians visual inspection | #6 |
| label nfs | #6 |
| nf locations | #9 |
| neural foraminal stenosis | #17 |
| classification nf | #18 |
| incorporates task | #18 |
| preliminary guess | #20 |
| severe ambiguities | #20 |
| stenosis nfs | #21 |
| localization grading | #26 |
| common spinal disease | #26 |
| aware structural | #30 |
| regression segmentation | #30 |
| nf class | #31 |
| label appearance | #32 |
| grading task | #47 |
| automated neural | #60 |
| supervised distance | #68 |
| task labels | #71 |
| neural foraminal | #77 |
| neural foramina | #92 |
| candidates severe | #95 |
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Prominent publications by Xiaoxu He
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 segmentation and area estimation of neural foramina with boundary regression model
[ PUBLICATION ]
Accurate segmentation and area estimation of neural foramina from both CT and MR images are essential to clinical diagnosis of neural foramina stenosis. Existing clinical routine, relying on physician's purely manual segmentation, becomes very tedious, laborious, and inefficient. Automated segmentation is highly desirable but faces big challenges from diverse boundary, local weak/no boundary, and intra/inter-modality intensity inhomogeneity. In this paper, a novel boundary regression ...
| Known for Neural Foramina | Automated Segmentation | Proposed Framework | Regression Model | Support Vector |
Automated Diagnosis of Neural Foraminal Stenosis Using Synchronized Superpixels Representation
[ PUBLICATION ]
Neural foramina stenosis (NFS), as a common spine disease, affects \documentclass[12pt]{minimal}
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\begin{document}$$80\,\%$$\end{document} of people. Clinical diagnosis by physicians’ manual segmentation is inefficient and laborious. Automated diagnosis is highly desirable ...
| Known for Neural Foramina | Automated Diagnosis | Clinical Tool |
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 |
