Abstract
The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.
Highlights
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 are two hyperparameters to be configured on simple linear iterative clustering (SLIC) algorithm: the number of target superpixels (K) and the weight parameter (m), which balances the contribution of the spectral and the spatial distance
We evaluated the effect of varying both K and m in the mean intra-cluster distance (Equation (11)), where c j is the centroid of the superpixel j, and xi represents a pixel on the image assigned to such a superpixel
Summary
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. 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.
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