DeepDOF-Se: Affordable Deep-Learning Microscopy Platform for Slide-Free Histology

Authors

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
Ashok Veeraraghavan

Publication Date

4-5-2024

Journal

Nature Communications

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.

Keywords

Microscopy, Deep Learning, Fluorescent Dyes, Hematoxylin, Eosine Yellowish-(YS)

DOI

10.1038/s41467-024-47065-2

PMID

38580633

PMCID

PMC10997797

PubMedCentral® Posted Date

4-5-2024

PubMedCentral® Full Text Version

Post-print

Recommended Citation

Jin, Lingbo; Tang, Yubo; Coole, Jackson B; Tan, Melody T; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T; Williams, Michelle D; Vigneswaran, Nadarajah; Gillenwater, Ann M; Richards-Kortum, Rebecca R; and Veeraraghavan, Ashok, "DeepDOF-Se: Affordable Deep-Learning Microscopy Platform for Slide-Free Histology" (2024). Faculty, Staff and Student Publications. 102.
https://digitalcommons.library.tmc.edu/uthdb_docs/102

Published Open-Access

yes