Related Experiment Video
Updated: Mar 3, 2026

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
Published on: February 9, 2024
PathoSpotter-K: A computational tool for the automatic identification of glomerular lesions in histological images of
George O Barros1, Brenda Navarro2, Angelo Duarte1
1Universidade Estadual de Feira de Santana (UEFS) - Programa de Pos-Graduacao em Computacao Aplicada, Feira de Santana, Bahia, Brazil.
Abstract:
PathoSpotter is a computational system designed to assist pathologists in teaching about and researching kidney diseases. PathoSpotter-K is the version that was developed to detect nephrological lesions in digital images of kidneys. Here, we present the results obtained using the first version of PathoSpotter-K, which uses classical image processing and pattern recognition methods to detect proliferative glomerular lesions with an accuracy of 88.3 ± 3.6%. Such performance is only achieved by similar systems if they use images of cell in contexts that are much less complex than the glomerular structure. The results indicate that the approach can be applied to the development of systems designed to train pathology students and to assist pathologists in determining large-scale clinicopathological correlations in morphological research.

