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Accurate intraoperative diagnosis is essential for providing safe andeffective care during brain tumor surgery. Our standard-of-care diagnosticmethods are time, resource, and labor intensive, which restricts access tooptimal surgical treatments. To address these limitations, we propose analternative workflow that combines stimulated Raman histology (SRH), a rapidoptical imaging method, with deep learning-based automated interpretation ofSRH images for intraoperative brain tumor diagnosis and real-time surgicaldecision support. Here, we present OpenSRH, the first public dataset ofclinical SRH images from 300+ brain tumors patients and 1300+ unique wholeslide optical images. OpenSRH contains data from the most common brain tumorsdiagnoses, full pathologic annotations, whole slide tumor segmentations, rawand processed optical imaging data for end-to-end model development andvalidation. We provide a framework for patch-based whole slide SRHclassification and inference using weak (i.e. patient-level) diagnostic labels.Finally, we benchmark two computer vision tasks: multiclass histologic braintumor classification and patch-based contrastive representation learning. Wehope OpenSRH will facilitate the clinical translation of rapid optical imagingand real-time ML-based surgical decision support in order to improve theaccess, safety, and efficacy of cancer surgery in the era of precisionmedicine. Dataset access, code, and benchmarks are available atopensrh.mlins.org.

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology