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Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

Medicine and Health

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

R. Leon, H. Fabelo, et al.

This research delves into the innovative combination of hyperspectral imaging and machine learning to enhance intraoperative brain tumor detection. Conducted by a team of experts, the study presents a benchmark that could lead to the development of real-time decision support tools in neurosurgery, showcasing a promising median macro F1-Score of 70.2%.

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Playback language: English
Abstract
This paper investigates the use of hyperspectral imaging (HSI) combined with machine learning for intraoperative brain tumor detection. A robust k-fold cross-validation approach was used to evaluate the performance of HSI in identifying and delineating brain tumors. Analysis of an in-vivo brain database (61 HS images from 34 patients) achieved a median macro F1-Score of 70.2 ± 7.9% on the test set using both spectral and spatial information. The study provides a benchmark for further development of real-time decision support tools in neurosurgery.
Publisher
npj Precision Oncology
Published On
Nov 14, 2023
Authors
Raquel Leon, Himar Fabelo, Samuel Ortega, Ines A. Cruz-Guerrero, Daniel Ulises Campos-Delgado, Adam Szolna, Juan F. Piñeiro, Carlos Espino, Aruma J. O'Shanahan, Maria Hernandez, David Carrera, Sara Bisshopp, Coralia Sosa, Francisco J. Balea-Fernandez, Jesus Morera, Bernardino Clavo, Gustavo M. Callico
Tags
hyperspectral imaging
machine learning
brain tumor detection
intraoperative
cross-validation
decision support tools
neurosurgery

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