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.
Cancer affects one in three people worldwide. Surgery remains the primary curative option for localized cancers, but good prognoses require complete removal of primary tumors and timely recognition of metastases. To expand surgical capabilities and enhance patient outcomes, we developed a six-channel color/near-infrared image sensor inspired by the mantis shrimp visual system that enabled near-infrared fluorescence image guidance during surgery. The mantis shrimp’s unique eye, which maximizes the number of photons contributing to and the amount of information contained in each glimpse of its surroundings, is recapitulated in our single-chip imaging system that integrates arrays of vertically stacked silicon photodetectors and pixelated spectral filters. To provide information about tumor location unavailable from a single instrument, we tuned three color channels to permit an intuitive perspective of the surgical procedure and three near-infrared channels to permit multifunctional imaging of optical probes highlighting cancerous tissue. In nude athymic mice bearing human prostate tumors, our image sensor enabled simultaneous detection of two tumor-targeted fluorophores, distinguishing diseased from healthy tissue in an estimated 92% of cases. It also permitted extraction of near-infrared structured illumination enabling the mapping of the three-dimensional topography of tumors and surgical sites to within 1.2-mm error. In the operating room, during surgical resection in 18 patients with breast cancer, our image sensor further enabled sentinel lymph node mapping using clinically approved near-infrared fluorophores. The flexibility and performance afforded by this simple and compact architecture highlights the benefits of biologically inspired sensors in image-guided surgery.
more » « less- Award ID(s):
- 2030421
- NSF-PAR ID:
- 10226530
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science Translational Medicine
- Volume:
- 13
- Issue:
- 592
- ISSN:
- 1946-6234
- Page Range / eLocation ID:
- Article No. eaaw7067
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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