Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging

• URL: http://arxiv.org/abs/2302.02884v1
• Date: Mon, 6 Feb 2023 15:52:03 GMT
• Title: Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
• Authors: Tommaso Giannantonio (1), Anna Alperovich (1), Piercosimo Semeraro (1 and 2), Manfredo Atzori (2 and 3), Xiaohan Zhang (4), Christoph Hauger (4), Alexander Freytag (1), Siri Luthman (5), Roeland Vandebriel (5), Murali Jayapala (5), Lien Solie (6), and Steven de Vleeschouwer (6) ((1) Carl Zeiss AG, (2) U. Padua, (3) HES-SO Valais, (4) Carl Zeiss Meditec AG, (5) IMEC, (6) KU and UZ Leuven)
• Abstract summary: Surgery for gliomas (intrinsic brain tumors) is challenging due to the infiltrative nature of the lesion. No real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. We build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance.
• Score: 37.21885467891782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468-787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

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