Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy
Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. Howe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3530 |
| _version_ | 1827671337863217152 |
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| author | Sarinporn Visitsattapongse Kitsada Thadson Suejit Pechprasarn Nuntachai Thongpance |
| author_facet | Sarinporn Visitsattapongse Kitsada Thadson Suejit Pechprasarn Nuntachai Thongpance |
| author_sort | Sarinporn Visitsattapongse |
| collection | DOAJ |
| description | Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning. |
| first_indexed | 2024-03-10T03:40:51Z |
| format | Article |
| id | doaj.art-42551351adec4fdab204da581bbac200 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:40:51Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-42551351adec4fdab204da581bbac2002023-11-23T09:19:49ZengMDPI AGSensors1424-82202022-05-01229353010.3390/s22093530Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging MicroscopySarinporn Visitsattapongse0Kitsada Thadson1Suejit Pechprasarn2Nuntachai Thongpance3Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandCollege of Biomedical Engineering, Rangsit University, Pathum Thani 12000, ThailandCollege of Biomedical Engineering, Rangsit University, Pathum Thani 12000, ThailandQuantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.https://www.mdpi.com/1424-8220/22/9/3530phase retrieval algorithmquantitative phase imagingsurface plasmon microscopyinstrumentation |
| spellingShingle | Sarinporn Visitsattapongse Kitsada Thadson Suejit Pechprasarn Nuntachai Thongpance Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy Sensors phase retrieval algorithm quantitative phase imaging surface plasmon microscopy instrumentation |
| title | Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy |
| title_full | Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy |
| title_fullStr | Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy |
| title_full_unstemmed | Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy |
| title_short | Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy |
| title_sort | analysis of deep learning based phase retrieval algorithm performance for quantitative phase imaging microscopy |
| topic | phase retrieval algorithm quantitative phase imaging surface plasmon microscopy instrumentation |
| url | https://www.mdpi.com/1424-8220/22/9/3530 |
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