Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning

Philipp Gruening, Falk Nette, Noah Heldt, Ana Cristina Guerra de Souza, Erhardt Barth
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:219-227, 2021.

Abstract

With in-line holography, it is possible to record biological cells over time in a three-dimensional hydrogel without the need for staining, providing the capability of observing cell behavior in a minimally invasive manner. However, this setup currently requires computationally intensive image-reconstruction algorithms to determine the required cell statistics. In this work, we directly extract cell positions from the holographic data by using deep neural networks and thus avoid several reconstruction steps. We show that our method is capable of substantially decreasing the time needed to extract information from the raw data without loss in quality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-gruening21a, title = {Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning}, author = {Gruening, Philipp and Nette, Falk and Heldt, Noah and de Souza, Ana Cristina Guerra and Barth, Erhardt}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {219--227}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/gruening21a/gruening21a.pdf}, url = {https://proceedings.mlr.press/v143/gruening21a.html}, abstract = {With in-line holography, it is possible to record biological cells over time in a three-dimensional hydrogel without the need for staining, providing the capability of observing cell behavior in a minimally invasive manner. However, this setup currently requires computationally intensive image-reconstruction algorithms to determine the required cell statistics. In this work, we directly extract cell positions from the holographic data by using deep neural networks and thus avoid several reconstruction steps. We show that our method is capable of substantially decreasing the time needed to extract information from the raw data without loss in quality.} }
Endnote
%0 Conference Paper %T Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning %A Philipp Gruening %A Falk Nette %A Noah Heldt %A Ana Cristina Guerra de Souza %A Erhardt Barth %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-gruening21a %I PMLR %P 219--227 %U https://proceedings.mlr.press/v143/gruening21a.html %V 143 %X With in-line holography, it is possible to record biological cells over time in a three-dimensional hydrogel without the need for staining, providing the capability of observing cell behavior in a minimally invasive manner. However, this setup currently requires computationally intensive image-reconstruction algorithms to determine the required cell statistics. In this work, we directly extract cell positions from the holographic data by using deep neural networks and thus avoid several reconstruction steps. We show that our method is capable of substantially decreasing the time needed to extract information from the raw data without loss in quality.
APA
Gruening, P., Nette, F., Heldt, N., de Souza, A.C.G. & Barth, E.. (2021). Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:219-227 Available from https://proceedings.mlr.press/v143/gruening21a.html.

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