Segmentation of Intraoperative Glioblastoma Hyperspectral Images Using Self-Supervised U-Net++

Brain tumour resection yields many challenges for neurosurgeons and even though histopathological analysis can help to complete tumour elimination, it is not feasible due to the extent of time and tissue demand for margin inspection. This paper presents a novel attention-based self-supervised methodology to improve current research on medical hyperspectral imaging as a tool for computer-aided diagnosis. We designed a novel architecture comprising the U-Net++ and the attention mechanism on the spectral domain, trained in a self-supervised framework to exploit contrastive learning capabilities and overcome dataset size problems arising in medical scenarios. We operated fifteen hyperspectral images from the publicly available HELICoiD dataset. Enhanced by extensive data augmentation, transfer-learning and self-supervision, we measured accuracy, specificity and recall values above 90% in the automatic end-to-end segmentation of intraoperative glioblastoma hyperspectral images. We evaluated our outcomes with the ground truths produced by the HELICoiD project, obtaining results that are comparable concerning the gold-standard procedure.