Accurate identification of tumor boundaries during brain cancer surgery determines the quality of life of the patient. Different intraoperative guidance tools are currently employed during the resection tumor but having several limitations. Hyperspectral imaging (HSI) is arising as a label-free and non-ionizing technique that could assist neurosurgeons during surgical procedures. In this paper, an analysis between in-vivo and ex-vivo human brain tumor samples using HSI has been performed to evaluate the correlation between both types of samples. Spectral ratios of the oxygenated and deoxygenated hemoglobin were employed to distinguish between normal tissue, tumor tissue and blood vessels. A database composed by seven in-vivo and fourteen ex-vivo hyperspectral images obtained from seven different patients diagnosed with glioblastoma Grade IV, metastatic secondary breast cancer, meningioma Grade I and II, and astrocytoma (glioma) Grade II. 44,964 pixels labeled pixels were employed in this work. The proposed method achieved discrimination between different tissue types using the proposed spectral ratio. Comparison between in-vivo and ex-vivo samples indicated that ex-vivo samples generate higher hemoglobin ratios. Moreover, vascular enhanced maps were generated using the spectral ratio, targeting real-time intraoperative surgical assistance.
Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.
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