Localized Statistical Shape Models for Large-Scale Problems With Few Training Data-Reference-Cited by-同舟云学术

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Localized Statistical Shape Models for Large-Scale Problems With Few Training Data

Published:2022-09 Issue:9 Volume:69 Page:2947-2957
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Wilms Matthias¹^ORCID,Ehrhardt Jan²,Forkert Nils D.³^ORCID

Affiliation:

1. Department of Radiology and with the Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada

2. Institute of Medical Informatics, University of Lübeck, Germany

3. Departments of Radiology and Clinical Neurosciences and with the Alberta Children’s Hospital Research Institute, University of Calgary, Canada

Funder

University of Calgary

T. Chen Fong Postdoctoral Fellowship

Calgary Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/9863423/09732672.pdf?arnumber=9732672

Reference62 articles.

1. A Kernelized Multi-level Localization Method for Flexible Shape Modeling with Few Training Data

2. Boundary finding with prior shape and smoothness models

3. Data Driven Refinement of Active Shape Model Search

4. Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies;2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2022-12-06

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