Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.
Hollandi R., Moshkov N., Paavolainen L., Tasnadi E., Piccinini F., Horvath P. (2022). Nucleus segmentation: towards automated solutions. TRENDS IN CELL BIOLOGY, 32(4), 295-310 [10.1016/j.tcb.2021.12.004].
Nucleus segmentation: towards automated solutions
Piccinini F.;
2022
Abstract
Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.File | Dimensione | Formato | |
---|---|---|---|
Trends_in_Cell_Biology-2-72.pdf
accesso aperto
Tipo:
Postprint
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione
16.35 MB
Formato
Adobe PDF
|
16.35 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.