Quantitative phase microscopy using deep neural networks
Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data gener...
| Main Authors: | , , , |
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| Format: | Article |
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SPIE
2018
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| Online Access: | http://hdl.handle.net/1721.1/119144 https://orcid.org/0000-0002-7836-0431 https://orcid.org/0000-0002-4140-1404 |
| _version_ | 1826204035523280896 |
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| author | Li, Shuai Sinha, Ayan T Lee, Justin Barbastathis, George |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Li, Shuai Sinha, Ayan T Lee, Justin Barbastathis, George |
| author_sort | Li, Shuai |
| collection | MIT |
| description | Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved. |
| first_indexed | 2024-09-23T12:47:48Z |
| format | Article |
| id | mit-1721.1/119144 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:47:48Z |
| publishDate | 2018 |
| publisher | SPIE |
| record_format | dspace |
| spelling | mit-1721.1/1191442022-09-28T10:06:34Z Quantitative phase microscopy using deep neural networks Li, Shuai Sinha, Ayan T Lee, Justin Barbastathis, George Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Mechanical Engineering Li, Shuai Sinha, Ayan T Lee, Justin Barbastathis, George Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved. Singapore-MIT Alliance for Research and Technology (SMART) United States. Department of Energy. Computational Science Graduate Fellowship Program (DE-FG02-97ER25308) United States. Intelligence Advanced Research Projects Activity 2018-11-16T15:37:22Z 2018-11-16T15:37:22Z 2018-02 2018-10-29T19:37:38Z Article http://purl.org/eprint/type/ConferencePaper 9781510614918 9781510614925 http://hdl.handle.net/1721.1/119144 Li, Shuai, et al. “Quantitative Phase Microscopy Using Deep Neural Networks.” Quantitative Phase Imaging IV, 27 January, - February 1, 2018, San Francisco, California, edited by Gabriel Popescu and YongKeun Park, SPIE, 2018, p. 84. © SPIE. https://orcid.org/0000-0002-7836-0431 https://orcid.org/0000-0002-4140-1404 http://dx.doi.org/10.1117/12.2289056 Quantitative Phase Imaging IV Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf SPIE SPIE |
| spellingShingle | Li, Shuai Sinha, Ayan T Lee, Justin Barbastathis, George Quantitative phase microscopy using deep neural networks |
| title | Quantitative phase microscopy using deep neural networks |
| title_full | Quantitative phase microscopy using deep neural networks |
| title_fullStr | Quantitative phase microscopy using deep neural networks |
| title_full_unstemmed | Quantitative phase microscopy using deep neural networks |
| title_short | Quantitative phase microscopy using deep neural networks |
| title_sort | quantitative phase microscopy using deep neural networks |
| url | http://hdl.handle.net/1721.1/119144 https://orcid.org/0000-0002-7836-0431 https://orcid.org/0000-0002-4140-1404 |
| work_keys_str_mv | AT lishuai quantitativephasemicroscopyusingdeepneuralnetworks AT sinhaayant quantitativephasemicroscopyusingdeepneuralnetworks AT leejustin quantitativephasemicroscopyusingdeepneuralnetworks AT barbastathisgeorge quantitativephasemicroscopyusingdeepneuralnetworks |