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A Cell Segmentation/Tracking Tool Based on Machine Learning

Deter, Heather S., Dies, Marta, Cameron, Courtney C., Butzin, Nicholas C., Buceta, Javier·2019·Methods in Molecular Biology
DOI 10.1007/978-1-4939-9686-5_19
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Abstract

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.

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Research works printed but not linked: 24

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Metadata

Authors
Deter, H. S., Dies, M., Cameron, C. C., Butzin, N. C., & Buceta, J.
Venue
Methods in Molecular Biology
Publisher
Springer New York
Published
2019
Pages
399-422
ISSN
  • 1064-3745
  • 1940-6029
DOI
10.1007/978-1-4939-9686-5_19
Cited by
17
References
0
Language
en
Licence
www.springer.com/tdm (tdm)

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