Author

E. N. Henning

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Biology Disciplines

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Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:clustering· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

B. Provencher· 1 shared D. M. Patton· 1 shared E. M. R. Bigelow· 1 shared K. J. Jepsen· 1 shared M. Marsh· 1 shared N. Piche· 1 shared R. W. Goulet· 1 shared S. K. Carroll· 1 shared

Activity by Year

2021-2021 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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1 shared paperBioengineering

B. Provencher

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1 paper

Recent·Most cited

Deep learning-based segmentation of high-resolution computed tomography image data outperforms commonly used automatic bone segmentation methods

D. M. Patton, E. N. Henning, R. W. Goulet, S. K. Carroll, E. M. R. Bigelow, B. Provencher, N. Piche, M. Marsh, K. J. Jepsen, T. L. Bredbenner

This study compares deep learning-based segmentation using U-Net with thresholding methods for CT bone data, finding U-Net outperforms thresholding and can generalize across anatomical sites and scanners.

bioRxiv · Jul 27, 2021 · PreprintBioengineering