Trained neural network used in "OrganoidTracker: efficient cell tracking using machine learning and manual error correction"

Convolutional neural network and minimal set of Python scripts to detect nuclei in confocal microscopy images.

Identifier
DOI https://doi.org/10.17026/dans-274-a78v
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-ph-ffyn
Related Identifier https://doi.org/10.1101/2020.03.18.996421
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:161642
Provenance
Creator Kok, R.N.U (ORCID: 0000-0002-6214-681X); Hebert, L.
Publisher Data Archiving and Networked Services (DANS)
Contributor Huelsz-Prince, G.; Goos, Y.J.; Zheng, X.; Bozek, K.; Stephens, G.J.; Tans, S.J.; Zon, J.S. van; Okinawa Institute of Science and Technology
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; License: http://opensource.org/licenses/MIT; http://opensource.org/licenses/MIT
OpenAccess true
Representation
Language English
Resource Type Software
Format Google- Tensorflow 1.12; application/x-cmdi+xml
Discipline Basic Biological and Medical Research; Biology; Cell Biology; Life Sciences