Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells
Summary: Several hematologic diseases, including malaria, diabetes, and sickle cell anemia, result in a reduced red blood cell deformability. This deformability can be measured using a microfluidic device with channels of varying width. Nevertheless, it is challenging to algorithmically recognize la...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223026196 |
| _version_ | 1797388745753755648 |
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| author | Daniel T. Rademaker Joshua J. Koopmans Gwendolyn M.S.M. Thyen Aigars Piruska Wilhelm T.S. Huck Gert Vriend Peter A.C. ‘t Hoen Taco W.A. Kooij Martijn A. Huynen Nicholas I. Proellochs |
| author_facet | Daniel T. Rademaker Joshua J. Koopmans Gwendolyn M.S.M. Thyen Aigars Piruska Wilhelm T.S. Huck Gert Vriend Peter A.C. ‘t Hoen Taco W.A. Kooij Martijn A. Huynen Nicholas I. Proellochs |
| author_sort | Daniel T. Rademaker |
| collection | DOAJ |
| description | Summary: Several hematologic diseases, including malaria, diabetes, and sickle cell anemia, result in a reduced red blood cell deformability. This deformability can be measured using a microfluidic device with channels of varying width. Nevertheless, it is challenging to algorithmically recognize large numbers of red blood cells and quantify their deformability from image data. Deep learning has become the method of choice to handle noisy and complex image data. However, it requires a significant amount of labeled data to train the neural networks. By creating images of cells and mimicking noise and plasticity in those images, we generate synthetic data to train a network to detect and segment red blood cells from video-recordings, without the need for manually annotated labels. Using this new method, we uncover significant differences between the deformability of RBCs infected with different strains of Plasmodium falciparum, providing clues to the variation in virulence of these strains. |
| first_indexed | 2024-03-08T22:45:16Z |
| format | Article |
| id | doaj.art-7540ac269b4b44e495d44a52dc8cf53f |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T22:45:16Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-7540ac269b4b44e495d44a52dc8cf53f2023-12-17T06:41:08ZengElsevieriScience2589-00422023-12-012612108542Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cellsDaniel T. Rademaker0Joshua J. Koopmans1Gwendolyn M.S.M. Thyen2Aigars Piruska3Wilhelm T.S. Huck4Gert Vriend5Peter A.C. ‘t Hoen6Taco W.A. Kooij7Martijn A. Huynen8Nicholas I. Proellochs9Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsMedical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsRadboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsInstitute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the NetherlandsInstitute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the NetherlandsBaco Institute for Protein Science, Mindoro 5201, PhilippinesMedical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsRadboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsMedical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, the NetherlandsRadboud Center for Infectious Diseases, Medical Microbiology, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands; Corresponding authorSummary: Several hematologic diseases, including malaria, diabetes, and sickle cell anemia, result in a reduced red blood cell deformability. This deformability can be measured using a microfluidic device with channels of varying width. Nevertheless, it is challenging to algorithmically recognize large numbers of red blood cells and quantify their deformability from image data. Deep learning has become the method of choice to handle noisy and complex image data. However, it requires a significant amount of labeled data to train the neural networks. By creating images of cells and mimicking noise and plasticity in those images, we generate synthetic data to train a network to detect and segment red blood cells from video-recordings, without the need for manually annotated labels. Using this new method, we uncover significant differences between the deformability of RBCs infected with different strains of Plasmodium falciparum, providing clues to the variation in virulence of these strains.http://www.sciencedirect.com/science/article/pii/S2589004223026196Biological sciencesBiotechnologyComputer science |
| spellingShingle | Daniel T. Rademaker Joshua J. Koopmans Gwendolyn M.S.M. Thyen Aigars Piruska Wilhelm T.S. Huck Gert Vriend Peter A.C. ‘t Hoen Taco W.A. Kooij Martijn A. Huynen Nicholas I. Proellochs Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells iScience Biological sciences Biotechnology Computer science |
| title | Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells |
| title_full | Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells |
| title_fullStr | Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells |
| title_full_unstemmed | Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells |
| title_short | Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells |
| title_sort | quantifying the deformability of malaria infected red blood cells using deep learning trained on synthetic cells |
| topic | Biological sciences Biotechnology Computer science |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223026196 |
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