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OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
Cheng Jiang · Asadur Chowdury · Xinhai Hou · Akhil Kondepudi · Christian Freudiger · Kyle Conway · Sandra Camelo-Piragua · Daniel Orringer · Honglak Lee · Todd Hollon

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #1019

Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support. Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data for end-to-end model development and validation. We provide a framework for patch-based whole slide SRH classification and inference using weak (i.e. patient-level) diagnostic labels. Finally, we benchmark two computer vision tasks: multi-class histologic brain tumor classification and patch-based contrastive representation learning. We hope OpenSRH will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support in order to improve the access, safety, and efficacy of cancer surgery in the era of precision medicine.

Author Information

Cheng Jiang (University of Michigan)
Asadur Chowdury (University of Michigan - Ann Arbor)
Xinhai Hou (University of Michigan)
Akhil Kondepudi (University of Michigan - Ann Arbor)
Christian Freudiger (Invenio)
Kyle Conway (University of Michigan - Ann Arbor)
Sandra Camelo-Piragua (University of Michigan - Ann Arbor)
Daniel Orringer
Honglak Lee (LG AI Research / U. Michigan)
Todd Hollon (University of Michigan)

Brain tumor and machine learning researcher

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