Deep learning extended depth-of-field microscope for fast and slide-free histology
Copyright © 2020 the Author(s). Published by PNAS..
Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells-a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:117 |
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| Enthalten in: |
Proceedings of the National Academy of Sciences of the United States of America - 117(2020), 52 vom: 29. Dez., Seite 33051-33060 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jin, Lingbo [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 12.02.2021 Date Revised 17.12.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1073/pnas.2013571117 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM318833786 |
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| 520 | |a Copyright © 2020 the Author(s). Published by PNAS. | ||
| 520 | |a Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells-a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a end-to-end optimization | |
| 650 | 4 | |a extended depth-of-field microscopy | |
| 650 | 4 | |a pathology | |
| 650 | 4 | |a phase mask | |
| 700 | 1 | |a Tang, Yubo |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Coole, Jackson B |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Melody T |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xuan |e verfasserin |4 aut | |
| 700 | 1 | |a Badaoui, Hawraa |e verfasserin |4 aut | |
| 700 | 1 | |a Robinson, Jacob T |e verfasserin |4 aut | |
| 700 | 1 | |a Williams, Michelle D |e verfasserin |4 aut | |
| 700 | 1 | |a Gillenwater, Ann M |e verfasserin |4 aut | |
| 700 | 1 | |a Richards-Kortum, Rebecca R |e verfasserin |4 aut | |
| 700 | 1 | |a Veeraraghavan, Ashok |e verfasserin |4 aut | |
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