An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy

Abstract

Medical Imaging Computer Aided Diagnosis (CAD) systems could support physicians in several fields and recently are also applied in histopathology. The goal of this work is to design and test a novel CAD system module for the discrimination between glomeruli with a sclerotic and non-sclerotic condition, through the elaboration of histological images. The dataset was constituted by 26 kidney biopsies coming from 19 donors with Periodic Acid Schiff (PAS) staining. Preparation, digital acquisition and glomeruli annotations have been conducted by experts from the Department of Emergency and Organ Transplantation (DETO) of the University of Bari Aldo Moro (Italy). Starting from the annotated Regions Of Interest (ROIs), several feature extraction techniques were evaluated. Feature reduction and shallow artificial neural network were used for discriminating between the glomeruli classes. The mean and the best performances of the best ANN architecture were evaluated on an independent dataset. Metric comparison and analysis were performed to face the unbalanced dataset problem. Results on the test set asses that the proposed workflow, from the feature extraction to the supervised ANN approach, is consistent and reveals good performance in discriminating sclerotic and non-sclerotic glomeruli.