Deep learning meets topology-preserving active contours: towards scalable quantitative histology of cortical cytoarchitecture.

K. Thierbach; P.L. Bazin; F. Gavriilidis; E. Kirilina; C. Jager; M. Morawski; S. Geyer; Nikolaus Weiskopf; N. Scherf

Deep learning meets topology-preserving active contours: towards scalable quantitative histology of cortical cytoarchitecture.

K. Thierbach, P.L. Bazin, F. Gavriilidis, E. Kirilina, C. Jager, M. Morawski, S. Geyer, Nikolaus Weiskopf, N. Scherf

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

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Original language	English
Journal	bioRxiv
Publication status	E-pub ahead of print - 2019

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@article{e1da718c5f624ae7b1849c8093974868,

title = "Deep learning meets topology-preserving active contours: towards scalable quantitative histology of cortical cytoarchitecture.",

author = "K. Thierbach and P.L. Bazin and F. Gavriilidis and E. Kirilina and C. Jager and M. Morawski and S. Geyer and Nikolaus Weiskopf and N. Scherf",

year = "2019",

language = "English",

journal = "bioRxiv",

issn = "The preprint server for biology",

publisher = "Cold Spring Harbor Laboratory Press",

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TY - JOUR

T1 - Deep learning meets topology-preserving active contours: towards scalable quantitative histology of cortical cytoarchitecture.

AU - Thierbach, K.

AU - Bazin, P.L.

AU - Gavriilidis, F.

AU - Kirilina, E.

AU - Jager, C.

AU - Morawski, M.

AU - Geyer, S.

AU - Weiskopf, Nikolaus

AU - Scherf, N.

PY - 2019

Y1 - 2019

M3 - Article

JO - bioRxiv

JF - bioRxiv

SN - The preprint server for biology

ER -