# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 5 Conclusions  
In this work, we propose a combination of superpixel segmentation and supervised classification for the identification of GB tumor in HS images from brain histological slides. First, we modified and optimized the original SLIC segmentation algorithm for HS images. These modifications included the use of a spectral distance based on the spectral angle, and the modification of the global distance, which is a tradeoff between the spectral and the spatial distances between pixels in a neighborhood. After optimizing the hyperparameters of the SLIC algorithm to generate compact pixels, the superpixels were used to build a dataset where superpixels were labeled as tumor or non-tumor according to the pathologists' annotations. The data were then partitioned in patient-independent training, validation, and test sets. Finally, superpixels were classified using a linear SVM classifier.  
The results obtained with this methodology are promising, showing high sensitivity and specificity values for almost all the patients independently. On average, our proposed approach achieved 87% and 81% of sensitivity and specificity, respectively. Additionally, 8 out of the 13 patients obtained a sensitivity of 100%, and 3 out of the 13 had a sensitivity of 83-95%. Screening tests with high sensitivities could be useful clinically. The outcomes of this processing framework could potentially be used in a clinical application to flag which regions of the histological slide are of interest to be further analyzed by a pathologist, and then reduce whole-slide examination.  
Conceptualization, S.O., H.F., M.H., G.M.C., and B.F.; software, S.O., H.F., and M.H.; validation, S.O., H.F., and M.H.; investigation, S.O., H.F., and M.H.; resources, G.M.C. and B.F.; data curation, R.C. and M.d.l.L.P.; writing--original draft preparation, S.O., H.F., and M.H.; writing--review and editing, R.C., M.d.l.L.P., G.M.C., and B.F.; supervision, R.C., M.d.l.L.P., G.M.C. and B.F.; project administration, G.M.C. and B.F.; funding acquisition, G.M.C. and B.F. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the Cancer Prevention and Research Institute of Texas (CPRIT) grant RP190588. This work has been supported by the Spanish Government through PLATINO project (TEC2017-86722-C4-4-R), and the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHaCA project \"Hyperespectral identification of Brain tumors\" under Grant Agreement ProID2017010164. This work was completed while [PERSON] was beneficiary of a pre-doctoral grant given by the \"_Agencia Canaria de Investigacion, Innovation Sociadal de la Informacion (ACIISI)_\" of the \"_Conserjeria de Economia, Industria, Comercio y Concomitinto_\" of the \"_Gobierno de Canarias_\", which is part-financed by the European Social Fund (FSE) (_POC 2014-2020, Eje 3 Tema Prioriativa 74 (85%)_).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Written informed consent was obtained from all of the participant subjects, and the study protocol and consent procedures were approved by the _Comite Etico de Investigacion Clinica-Comite de Etica en la Investigacion (CEIC/CEI)_ of the University Hospital Doctor Negrin: _IdenTifacion Hiperespectral de tunores CerebrAles_ (ITHaCA), Code: 2019-001-1.