DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

Lingbo Jin, Yubo Tang, Jackson B. Coole, Melody T. Tan, Xuan Zhao, Hawraa Badaoui, Jacob T. Robinson, Michelle D. Williams, Nadarajah Vigneswaran, Ann M. Gillenwater, Rebecca R. Richards-Kortum () and Ashok Veeraraghavan ()
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Lingbo Jin: Rice University
Yubo Tang: Rice University
Jackson B. Coole: Rice University
Melody T. Tan: Rice University
Xuan Zhao: Rice University
Hawraa Badaoui: University of Texas MD Anderson Cancer Center
Jacob T. Robinson: Rice University
Michelle D. Williams: University of Texas MD Anderson Cancer Center
Nadarajah Vigneswaran: University of Texas Health Science Center at Houston School of Dentistry
Ann M. Gillenwater: University of Texas MD Anderson Cancer Center
Rebecca R. Richards-Kortum: Rice University
Ashok Veeraraghavan: Rice University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Date: 2024
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DOI: 10.1038/s41467-024-47065-2

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