Author

T. B. Usdin

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Research Focus:deconvolution· 2 Approach:deep learning· 2 Assay:fluorescence / live-cell microscopy· 1 Assay:imaging· 1 Organism:mouse· 1 Research Focus:spatial domain detection· 1 Organism:zebrafish· 1

Frequent Co-Authors

D. Colon-Ramos· 2 shared H. Liu· 2 shared H. Shroff· 2 shared H. Vishwasrao· 2 shared M. Guo· 2 shared Y. Li· 2 shared Y. Su· 2 shared Y. Wu· 2 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBiophysics

D. Colon-Ramos

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2 papers

Recent·Most cited

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

Y. Li, Y. Su, M. Guo, X. Han, J. Liu, H. Vishwasrao, X. Li, R. Christensen, T. Sengupta, M. Moyle, J. Chen, T. B. Usdin, D. Colon-Ramos, H. Liu, Y. Wu, H. Shroff

This paper introduces RLN, a deep learning method for 3D fluorescence microscopy deconvolution that improves speed, performance, and generalizability compared to existing methods.

bioRxiv · Mar 5, 2022 · PreprintBiophysics

Accelerating iterative deconvolution and multiview fusion by orders of magnitude

M. Guo, Y. Li, Y. Su, T. Lambert, D. D. Nogare, M. W. Moyle, L. H. Duncan, R. Ikegami, A. Santella, I. Rey-Suarez, D. Green, J. Chen, H. Vishwasrao, S. Ganesan, J. C. Waters, , H. Shroff

This paper introduces faster deconvolution and multiview fusion methods, achieving speedups of 10 to several thousand-fold over previous approaches.

bioRxiv · May 23, 2019 · PreprintBioengineering