A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics-Reference-Cited by-同舟云学术

A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics

Published:2020-07-09 Issue:3 Volume:47 Page:246-259
ISSN:0071-3260
Container-title:Evolutionary Biology
language:en
Short-container-title:Evol Biol

Author:

Devine Jay^ORCID,Aponte Jose D.^ORCID,Katz David C.^ORCID,Liu Wei,Vercio Lucas D. Lo^ORCID,Forkert Nils D.^ORCID,Marcucio Ralph^ORCID,Percival Christopher J.^ORCID,Hallgrímsson Benedikt^ORCID

Funder

National Institutes of Health

Canadian Institutes of Health Research

Natural Sciences and Engineering Research Council of Canada

Canadian Foundation for Innovation

Publisher

Springer Science and Business Media LLC

Subject

Ecology, Evolution, Behavior and Systematics

Link

https://link.springer.com/content/pdf/10.1007/s11692-020-09508-8.pdf

Reference67 articles.

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2. Adams, D. C., Rohlf, F. J., & Slice, D. E. (2013). A field comes of age: Geometric morphometrics in the 21st century. Hystrix,24(1), 7–14. https://doi.org/10.4404/hystrix-24.1-6283.

3. Attanasio, C., Nord, A. S., Zhu, Y., Blow, M. J., Li, Z., Denise, K., et al. (2014). Fine tuning of craniofacial morphology by distant-acting enhancers. Science,342(6157), 1–20. https://doi.org/10.1126/science.1241006.

4. Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage,54(3), 2033–2044. https://doi.org/10.1016/j.neuroimage.2010.09.025.

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