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@inbook{1883441,
author = {Ulman, Vladimír and Wiesner, David},
address = {Neuveden},
booktitle = {Biomedical Image Synthesis and Simulation - Methods and Applications},
doi = {http://dx.doi.org/10.1016/B978-0-12-824349-7.00028-1},
edition = {1st ed.},
editor = {Ninon Burgos, David Svoboda},
keywords = {Image synthesis; Deep learning; Cell segmentation; Ground-truth data; Biomedical application},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Neuveden},
isbn = {978-0-12-824349-7},
pages = {447-489},
publisher = {Elsevier},
title = {Review of cell image synthesis for image processing},
url = {http://dx.doi.org/10.1016/B978-0-12-824349-7.00028-1},
year = {2022}
}
TY - CHAP
ID - 1883441
AU - Ulman, Vladimír - Wiesner, David
PY - 2022
TI - Review of cell image synthesis for image processing
VL - The MICCAI Society book Series
PB - Elsevier
CY - Neuveden
SN - 9780128243497
KW - Image synthesis
KW - Deep learning
KW - Cell segmentation
KW - Ground-truth data
KW - Biomedical application
UR - http://dx.doi.org/10.1016/B978-0-12-824349-7.00028-1
N2 - Opposites attract, also in the biomedical field and during the processing of cell microscopy images. In the same spirit, image processing, the indispensable analyst tool, is often supported by image synthesis applications. Image synthesis is a methodology implemented in computer program intended to create artificial cell images similar to images from real microscopy. The generation of artificial images has had a stable tradition in image processing and is currently gaining more attention with the rising popularity of deep learning. This chapter reviews the current state of cell image synthesis, including terminology, broader context, goals, and peculiarities. It offers a brief historical introspection and, most importantly, surveys all contemporary methodology and applications. The light descriptions of procedural methods with explicit parameters and deep learning-based methods with implicit parameters, such as the generative adversarial networks, are also included. Last but not least, this chapter discusses what kind of artificial images and ground-truth data the methods generate, including the subsequent usage of this data for image processing such as cell segmentation or data augmentation for deep learning. Among the covered methods are approaches generating artificial cell microscopy images of fluorescence stained proteins, actin filaments, chromatin stained nuclei, membranes, and even populations of cells or full cells in differential inference contrast microscopy, to name a few. The generated data is often accompanied by ground truth annotation, whose forms are also discussed, including cell detection markers, full cell segmentation, and cell tracking data.
ER -
ULMAN, Vladimír and David WIESNER. Review of cell image synthesis for image processing. In Ninon Burgos, David Svoboda. \textit{Biomedical Image Synthesis and Simulation - Methods and Applications}. 1st ed. Neuveden: Elsevier, 2022, p.~447-489. The MICCAI Society book Series. ISBN~978-0-12-824349-7. Available from: https://dx.doi.org/10.1016/B978-0-12-824349-7.00028-1.
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