Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

ABSTRACT: Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery¹. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive^2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce⁴. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)^5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the op

SUBMITTER: Hollon TC 

PROVIDER: S-EPMC6960329 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature