Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery

SIGNIFICANCE: Quantitative phase imaging (QPI) can visualize cellular morphology and measure dry mass. Automated segmentation of QPI imagery is desirable for tracking neuron growth. Convolutional neural networks (CNNs) have provided state-of-the-art results for image segmentation. Improving the amou...

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Detalles Bibliográficos
Autores principales: Gil, Eddie M., Steelman, Zachary A., Sedelnikova, Anna, Bixler, Joel N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2023
Materias:
Research Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311234/
https://www.ncbi.nlm.nih.gov/pubmed/37398700
http://dx.doi.org/10.1117/1.NPh.10.3.035004

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311234/
https://www.ncbi.nlm.nih.gov/pubmed/37398700
http://dx.doi.org/10.1117/1.NPh.10.3.035004

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