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

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

Extended Blind End-member and Abundance Estimation with Spatial Total Variation for Hyperspectral Imaging.

Blind linear unmixing (BLU) methods allow the separation of multi and hyperspectral data into end-members and abundance maps in an unsupervised fashion. However, due to incident noise, the abundance maps can exhibit high presence of granularity. To address this problem, in this paper, we present a novel proposal for BLU that considers spatial coherence in the abundance estimations, through a total spatial variation component. The proposed BLU formulation is based on the blind end-member and abundance extraction perspective with total spatial variation (EBEAE-STV). In EBEAE-STV, internal abundances are added to incorporate the spatial coherence in the cost function, which is solved by a coordinates descent algorithm. The results with synthetic data show that the proposed algorithm can significantly decrease the granularity in the estimated abundances, and the estimation errors and computational times are lower compared to state of the art methodologies.Clinical relevance- The proper and robust estimation of end-members and their respective contributions (abundances) in multi-spectral and hyper-spectral images from the proposed EBEAE-STV methodology might provide useful information in several biomedical applications, such as chemometric analysis on different biological samples, tumor identification and brain tissue classification for hyper-spectral imaging, among others.