Reference : Red blood cell phenotyping from 3D confocal images using artificial neural networks.
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/49337
Red blood cell phenotyping from 3D confocal images using artificial neural networks.
English
Simionato, Greta [> >]
Hinkelmann, Konrad [> >]
Chachanidze, Revaz [> >]
Bianchi, Paola [> >]
Fermo, Elisa [> >]
van Wijk, Richard [> >]
Leonetti, Marc [> >]
Wagner, Christian mailto [University of Luxembourg > Department of Physics and Material Science]
Kaestner, Lars [> >]
Quint, Stephan [> >]
2021
PLoS computational biology
17
5
e1008934
Yes (verified by ORBilu)
1553-734X
1553-7358
[en] Automation ; Case-Control Studies ; Erythrocytes/cytology/immunology ; Humans ; Image Processing, Computer-Assisted/methods ; Microscopy, Confocal/methods ; Neural Networks, Computer ; Reproducibility of Results
[en] The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.
http://hdl.handle.net/10993/49337

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