Zusammenfassung
We introduce an image-based strategy that builds on morphological cell profiling with the purpose of predicting canonical hypertrophy stimulators as proxies for pathomechanisms in cardiology. The traditional approach relies on extracting handcrafted morphological features from unlabeled cell image data in order to reason about cell biology. In this work we employ transformer networks that automatically learn features that help identify which hypertrophy stimulator has been applied on imaged cardiomyocytes. Numerical results illustrate the high predictive performance of this type of neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Caicedo J, Cooper S, Heigwer F, Warchal S, Qiu P, Molnar C et al. Data-analysis strategies for image-based cell profiling. Nat Methods. 2017;14(9):849–63.
Furkel J, Knoll M, Din S, Bogert N, Seeger T, Frey N et al. C-MORE: a high content single cell morphology assay for cardiovascular medicine. Cell Rep Med (accepted). 2021.
Carpenter A, Jones T, Lamprecht M, Clarke C, Kang I, Friman O et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;10(7):849–63.
Singh S Carpenter AE GA. Increasing the content of high-content screening: an overview. J Biomol Screen. 2014;19(5):640–650.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30.
Peng T, Thorn K, Schroeder T,Wang L, Theis FJ, Marr C et al. A BaSiC tool for background and shading correction of optical microscopy images. Nat Commun. 2017;8:14836.
Forster B, Van De Ville D, Berent J, Sage D, Unser M. Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images. Microsc Res Tech. 2004;65(1-2):33–42.
Schmidt U, Weigert M, Broaddus C, Myers G. Cell detection with star-convex polygons. MICCAI 2018. Ed. by Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G. Vol. 11071. (LNCS). Springer, 2018:265–73.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T et al. An image is worth 16x16 words: transformers for image recognition at scale. ICLR. 2021.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Plier, J. et al. (2022). Learning Features via Transformer Networks for Cardiomyocyte Profiling. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-658-36932-3_37
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-36931-6
Online ISBN: 978-3-658-36932-3
eBook Packages: Computer Science and Engineering (German Language)