Deep learning extended depth-of-field microscope for fast and slide-free histology
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,...
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| Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS Jg. 117; H. 52; S. 33051 |
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| Sprache: | Englisch |
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29.12.2020
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Coole, Jackson B Williams, Michelle D Badaoui, Hawraa Robinson, Jacob T Gillenwater, Ann M Wu, Yicheng Zhao, Xuan Richards-Kortum, Rebecca R Jin, Lingbo Tang, Yubo Tan, Melody T Veeraraghavan, Ashok |
| Author_xml | – sequence: 1 givenname: Lingbo surname: Jin fullname: Jin, Lingbo organization: Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005 – sequence: 2 givenname: Yubo orcidid: 0000-0003-2568-8940 surname: Tang fullname: Tang, Yubo organization: Department of Bioengineering, Rice University, Houston, TX 77005 – sequence: 3 givenname: Yicheng surname: Wu fullname: Wu, Yicheng organization: Department of Applied Physics, Rice University, Houston, TX 77005 – sequence: 4 givenname: Jackson B orcidid: 0000-0002-8821-4139 surname: Coole fullname: Coole, Jackson B organization: Department of Bioengineering, Rice University, Houston, TX 77005 – sequence: 5 givenname: Melody T orcidid: 0000-0001-9918-4062 surname: Tan fullname: Tan, Melody T organization: Department of Bioengineering, Rice University, Houston, TX 77005 – sequence: 6 givenname: Xuan surname: Zhao fullname: Zhao, Xuan organization: Department of Applied Physics, Rice University, Houston, TX 77005 – sequence: 7 givenname: Hawraa surname: Badaoui fullname: Badaoui, Hawraa organization: Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030 – sequence: 8 givenname: Jacob T orcidid: 0000-0002-3509-3054 surname: Robinson fullname: Robinson, Jacob T organization: Department of Bioengineering, Rice University, Houston, TX 77005 – sequence: 9 givenname: Michelle D surname: Williams fullname: Williams, Michelle D organization: Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030 – sequence: 10 givenname: Ann M surname: Gillenwater fullname: Gillenwater, Ann M organization: Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030 – sequence: 11 givenname: Rebecca R orcidid: 0000-0003-2347-9467 surname: Richards-Kortum fullname: Richards-Kortum, Rebecca R email: rkortum@rice.edu, vashok@rice.edu organization: Department of Bioengineering, Rice University, Houston, TX 77005; rkortum@rice.edu vashok@rice.edu – sequence: 12 givenname: Ashok orcidid: 0000-0001-5043-7460 surname: Veeraraghavan fullname: Veeraraghavan, Ashok email: rkortum@rice.edu, vashok@rice.edu organization: Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005; rkortum@rice.edu vashok@rice.edu |
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| Keywords | deep learning end-to-end optimization pathology phase mask extended depth-of-field microscopy |
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| Title | Deep learning extended depth-of-field microscope for fast and slide-free histology |
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