# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 1 Introduction  
Digital and computational pathology utilizes digitized images, typically RGB, of histology specimens for the creation of algorithms to aid pathologists in the diagnosis of diseases [1]. There may be information in the electromagnetic spectrum, both visible and beyond, that could be beneficial. Therefore, hyperspectral (HS) digital histology has emerged to explore if it can provide better diagnostic information than RGB (Red, Green, and Blue) imagery. Hyperspectral imaging (HSI) obtains the spectral data of an object using a HS optical sensor and is label-free [2], which can be applied readily to digitized histology. The main advantage of HSI is the possibility of exploiting both the spatial and the spectral information of the HS data. Although HSI has shown good performance in the detection of different diseases in histopathological applications, this technology still has to face different challenges. One of those challenges is related to the heterogeneity in image processing methods used to extract information from the HS data. Most of these image processing methods consist of application of Machine Learning (ML) methods. There are two main types of ML approaches. In feature learning approaches, the methods are devoted to learning directly from all the data features, while in deep learning (DL) approaches the important features of the data are also learned from the model. In HS computational pathology, some researchers have successfully used feature learning methods, where the classification is based only in the spectral information of the sample. Some examples of these approaches are the detection of human prostate cancer [3], colorectal cancer detection [4], or pancreatic ductal carcinoma [5]. Other researchers have used feature extraction methods able to exploit both the spatial and the spectral information of the HS data. The joint exploitation of the spatial and the spectral information has been used in HS computational pathology for prostate cancer [6] and for the diagnosis of acute lymphoblastic leukemia [7]. The main benefit of DL models, especially Convolutional Neural Networks (CNNs), is their ability to learn simultaneously which spectral and spatial features are relevant to differentiate between different types of tissue. Some examples of DL models in HS histopathology are the detection of different types of cancer cells within colon samples [8] or the identification of mitotic cells in breast cancer slides [9]. However, the lack of large and publicly available datasets makes it difficult for the research community to determine which approach is the most appropriate to extract information from medical HS data. Previously, we demonstrated glioblastoma (GB) tumor detection, a malignant high-grade glioma of the central nervous system, in pathological slides using HSI and patch-based CNNs [10]. As already mentioned, in CNNs both spectral and spatial information are used to learn the differences between tumor and non-tumor samples, but the patch-based regions were limited to square patches, which may group together regions of distinct micro-anatomical structures within the same patch. In this work, we propose a method where we first extract the most relevant spectral features from a HS image using a superpixel algorithm, and then we apply supervised machine learning algorithms to retrieve information about the diagnosis of brain histological slides. The final classification of the superpixels spectra was tested using both feature learning and DL approaches. One of the main motivations of this work is to determine if using only the spectral information is sufficient to discriminate between tumor and non-tumor tissues within a histological slide. In a standard digital image, each pixel contains intensity or color information. Methods for image processing often divide the image into blocks or patches, which are typically square. However, pixels can be grouped or clustered into similar, connected pixels called superpixels.