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Glioblastoma surgical resection is a problematic mission for neurosurgeons. Tumor complete resection improves patients healing chances and prognosis, whilst excessive resection could lead to neurological deficits. Nevertheless, surgeons' sight hardly traces the tumor's extent and boundaries. Indeed, most surgical processes result in subtotal resections. Histopathological testing might enable complete tumor elimination, though it is not feasible due to the time required for tissue investigation. Several studies reported tumor cells having unique molecular signatures and properties. Hyperspectral Imaging (HSI) is an emerging, non-contact, non-ionizing, label-free and minimally invasive optical imaging technique able to extract information concerning the observed tissue at the molecular level. Here, we exploited extensive data augmentation, transfer learning, the U-Net++ and the DeepLab-V3+ architectures to perform the automatic end-to-end segmentation of intraoperative glioblastoma hyperspectral images meeting competitive processing times and segmentation results concerning the gold-standard procedure. Based on ground truths provided by the HELICoiD framework, we dramatically improved HSIs processing times, enabling the end-to-end segmentation of glioblastomas targeting the real-time processing to be employed during open craniotomy in surgery, thus improving the gold-standard ML pipeline. We measured competitive inference times concerning the standard CUDA environment offered by MatLab 2020a. The HELICoiD fastest parallel version took 1.68 s to elaborate the most prominent image of the database, whilst our methodology performs segmentation inference in 0.29 ± 0.17 s, hence being real-time compliant concerning the 21 seconds constraint imposed on processing. Furthermore, we evaluated our segmentation results qualitatively and quantitatively regarding the ground truth produced by HELICoiD.
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