Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.

Autor: Blokker M; Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. m.blokker@vu.nl., Hamer PCW; Department of Neurosurgery, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands., Wesseling P; Department of Pathology, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands., Groot ML; Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands., Veta M; Medical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Jul 05; Vol. 12 (1), pp. 11334. Date of Electronic Publication: 2022 Jul 05.
DOI: 10.1038/s41598-022-15423-z
Abstrakt: Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon's ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.
(© 2022. The Author(s).)
Databáze: MEDLINE
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