| Autor: |
Lingbo Jin, Yubo Tang, Coole, Jackson B., Tan, Melody T., Xuan Zhao, Badaoui, Hawraa, Robinson, Jacob T., Williams, Michelle D., Vigneswaran, Nadarajah, Gillenwater, Ann M., Richards-Kortum, Rebecca R., Veeraraghavan, Ashok |
| Zdroj: |
Nature Communications; 4/5/2024, Vol. 15 Issue 1, p1-14, 14p, 7 Graphs |
| Abstrakt: |
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 tis- sue 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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