>Introduction: Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. In this research work, a multidisciplinary team, made up of pathologists and engineers, presents a proof of concept on the use of HSI analysis in order to automatically detect human brain tumour tissue from pathological slides. The samples were acquired from four different patients diagnosed with high-grade gliomas. Based on the diagnosis provided by pathologists, a spectral library containing spectra from healthy and tumour tissues was created. Data were finally processed using three different supervised machine learning algorithms. >Materials and methods: An acquisition system consisting of a HSI camera coupled to a microscope was developed to capture the hyperspectral images from pathology slides. The spectral sampling was done in the spectral range from 400 nm to 1000 nm with a spectral resolution of 2.8 nm. The biological samples consisted of biopsies of human brain tissue resected during surgery that followed a histological process, whereby tissue specimens were prepared for sectioning, staining and diagnosis. Only spectral characteristics of the data were taken into account. The inputs of the classifiers were the spectral signatures from healthy and tumour pixels. Three different supervised machine learning algorithms were employed: Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Random Forests (RF). >Results: The automatic diagnosis provided by the supervised classifiers shows a very high discrimination rate between healthy and tumour tissue, with high specificity and sensitivity above 90.83% and 94.55% respectively. Although all classifiers provide an accurate discrimination between healthy and tumour tissue, ANN presents the most accurate results with a specificity of 98.72% and a sensitivity of 97.71%. >Conclusions: This research work presents a proof of concept in the use of HSI for automatically detecting brain tumour tissue in pathological slides. HSI can obtain an accurate diagnosis without using the morphological features of tissues, being a suitable complement to the current analysis methods, assisting pathologists to analyse the slides without having to spend a long time in the examination of each sample.FUNDING: This work has been supported by the European Commission through the FP7 FET Open programme ICT- 2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant Agreement 618080.
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