Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors
Tomographic phase microscopy (TPM) in flow cytometry is one of the most promising computational imaging techniques for the quantitative 3-dimensional (3D) analysis of unstained single cells. Continuous cells’ flow, combined with the stain-free mode, can assure the high-throughput collection of quant...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2023-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0010 |
| _version_ | 1827932636958425088 |
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| author | Pasquale Memmolo Daniele Pirone Daniele Gaetano Sirico Lisa Miccio Vittorio Bianco Ahmed Bassam Ayoub Demetri Psaltis Pietro Ferraro |
| author_facet | Pasquale Memmolo Daniele Pirone Daniele Gaetano Sirico Lisa Miccio Vittorio Bianco Ahmed Bassam Ayoub Demetri Psaltis Pietro Ferraro |
| author_sort | Pasquale Memmolo |
| collection | DOAJ |
| description | Tomographic phase microscopy (TPM) in flow cytometry is one of the most promising computational imaging techniques for the quantitative 3-dimensional (3D) analysis of unstained single cells. Continuous cells’ flow, combined with the stain-free mode, can assure the high-throughput collection of quantitative and informative 3D data. TPM promises to allow rapid cells’ screening by a nondestructive technique and with statistically relevant data. The current leading-edge research aimed at developing TPM systems in flow cytometry has already demonstrated the possibility of acquiring thousands of single-cell tomograms. Nevertheless, a key unsolved problem exists about the efficient storage and easy handling of such a huge amount of 3D data that prevents rapid analysis for cell diagnosis. Here, we show, for the first time, an effective encoding strategy of single-cell tomograms that can completely overcome this critical bottleneck. Essentially, by using the 3D version of Zernike polynomials, we demonstrate that the 3D refractive index distribution of a cell can be straightforwardly encoded in 1D with negligible information loss (<1%), thus greatly streamlining the data handling and storage. The performance analysis of the proposed method has been first assessed on simulated tomographic cell phantom, while the experimental validation has been extensively proofed on tomographic data from experiments with different cell lines. The results achieved here imply an intriguing breakthrough for TPM that promises to unlock computational pipelines for analyzing 3D data that were unattainable until now. |
| first_indexed | 2024-03-13T07:12:33Z |
| format | Article |
| id | doaj.art-0c706bf5111c42e8aa3f06a7bb5ad104 |
| institution | Directory Open Access Journal |
| issn | 2771-5892 |
| language | English |
| last_indexed | 2024-03-13T07:12:33Z |
| publishDate | 2023-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Intelligent Computing |
| spelling | doaj.art-0c706bf5111c42e8aa3f06a7bb5ad1042023-06-05T16:40:11ZengAmerican Association for the Advancement of Science (AAAS)Intelligent Computing2771-58922023-01-01210.34133/icomputing.0010Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike DescriptorsPasquale Memmolo0Daniele Pirone1Daniele Gaetano Sirico2Lisa Miccio3Vittorio Bianco4Ahmed Bassam Ayoub5Demetri Psaltis6Pietro Ferraro7CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.EPFL, Ecole Polytechnique Fédérale de Lausanne, Optics Laboratory, CH-1015 Lausanne, Switzerland.EPFL, Ecole Polytechnique Fédérale de Lausanne, Optics Laboratory, CH-1015 Lausanne, Switzerland.CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy.Tomographic phase microscopy (TPM) in flow cytometry is one of the most promising computational imaging techniques for the quantitative 3-dimensional (3D) analysis of unstained single cells. Continuous cells’ flow, combined with the stain-free mode, can assure the high-throughput collection of quantitative and informative 3D data. TPM promises to allow rapid cells’ screening by a nondestructive technique and with statistically relevant data. The current leading-edge research aimed at developing TPM systems in flow cytometry has already demonstrated the possibility of acquiring thousands of single-cell tomograms. Nevertheless, a key unsolved problem exists about the efficient storage and easy handling of such a huge amount of 3D data that prevents rapid analysis for cell diagnosis. Here, we show, for the first time, an effective encoding strategy of single-cell tomograms that can completely overcome this critical bottleneck. Essentially, by using the 3D version of Zernike polynomials, we demonstrate that the 3D refractive index distribution of a cell can be straightforwardly encoded in 1D with negligible information loss (<1%), thus greatly streamlining the data handling and storage. The performance analysis of the proposed method has been first assessed on simulated tomographic cell phantom, while the experimental validation has been extensively proofed on tomographic data from experiments with different cell lines. The results achieved here imply an intriguing breakthrough for TPM that promises to unlock computational pipelines for analyzing 3D data that were unattainable until now.https://spj.science.org/doi/10.34133/icomputing.0010 |
| spellingShingle | Pasquale Memmolo Daniele Pirone Daniele Gaetano Sirico Lisa Miccio Vittorio Bianco Ahmed Bassam Ayoub Demetri Psaltis Pietro Ferraro Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors Intelligent Computing |
| title | Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors |
| title_full | Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors |
| title_fullStr | Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors |
| title_full_unstemmed | Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors |
| title_short | Loss Minimized Data Reduction in Single-Cell Tomographic Phase Microscopy Using 3D Zernike Descriptors |
| title_sort | loss minimized data reduction in single cell tomographic phase microscopy using 3d zernike descriptors |
| url | https://spj.science.org/doi/10.34133/icomputing.0010 |
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