Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus

The Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which...

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Autores principales:	Schmidt Dennis, Rausch Andreas, Schanze Thomas
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/2a4c9dda7a744059afb81ee99bffa149
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spelling	oai:doaj.org-article:2a4c9dda7a744059afb81ee99bffa1492021-12-05T14:10:43ZDeep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus2364-550410.1515/cdbme-2020-3129https://doaj.org/article/2a4c9dda7a744059afb81ee99bffa1492020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3129https://doaj.org/toc/2364-5504The Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.Schmidt DennisRausch AndreasSchanze ThomasDe GruyterarticleMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 501-504 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Schmidt Dennis Rausch Andreas Schanze Thomas Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
description	The Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.
format	article
author	Schmidt Dennis Rausch Andreas Schanze Thomas
author_facet	Schmidt Dennis Rausch Andreas Schanze Thomas
author_sort	Schmidt Dennis
title	Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
title_short	Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
title_full	Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
title_fullStr	Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
title_full_unstemmed	Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus
title_sort	deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with marburg virus
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/2a4c9dda7a744059afb81ee99bffa149
work_keys_str_mv	AT schmidtdennis deeplearningbasedrecognitionofcellstructuresinfluorescencemicroscopysequenceswithrespecttotheirmorphologyoncellsinfectedwithmarburgvirus AT rauschandreas deeplearningbasedrecognitionofcellstructuresinfluorescencemicroscopysequenceswithrespecttotheirmorphologyoncellsinfectedwithmarburgvirus AT schanzethomas deeplearningbasedrecognitionofcellstructuresinfluorescencemicroscopysequenceswithrespecttotheirmorphologyoncellsinfectedwithmarburgvirus
_version_	1718371781166759936

Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus

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