OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histologyDownload PDF

Published: 17 Sept 2022, Last Modified: 23 May 2023NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Stimulated Raman Histology, Computer Vision, Convolutional Neural Network, Vision Transformer, Contrastive Learning, Representation Learning
Abstract: 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 Statement: Yes
TL;DR: OpenSRH is the first ever publicly available stimulated Raman histology (SRH) dataset and benchmark, which will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support.
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License: The data is released under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), and the companion source code is released under MIT license.
Supplementary Material: pdf
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