A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmenta...
Saved in:
Main Authors: | Katharina Löffler, Tim Scherr, Ralf Mikut |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/c2468105f9d34cb293f7a8ada0e5f24e |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres in hiPSC-derived cardiomyocytes.
by: Bill Zhao, et al.
Published: (2021) -
A route pruning algorithm for an automated geographic location graph construction
by: Christoph Schweimer, et al.
Published: (2021) -
Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.
by: Moritz Böhland, et al.
Published: (2021) -
Strain-tunable van der Waals interactions in few-layer black phosphorus
by: Shenyang Huang, et al.
Published: (2019) -
Author Correction: Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography
by: Maximilian W. M. Wintergerst, et al.
Published: (2021)