How feasible is it to segment human glomerulus with a model trained on mouse histology images? - Souza, Luiz; Silva, Jefferson; Chagas, Paulo; Duarte, Angelo; dos-Santos, Washington LC; Oliveira, Luciano (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2022: Anais do XXII Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2022.222671, 2022)
Many genetic, physiological and structural characteristics of internal organs are shared by mice and humans. Hence, mice are frequently used in experimental model of human diseases. Although this is an indisputable truth in medicine, there is an avenue to go in computational pathology, where digital images are the main objects of investigation. Considering the lack of study about knowledge transfer between mice and humans concerning machine learning models, we propose investigating if it is possible to segment glomeruli in human WSIs by training deep learning models on mouse data only. Three different semantic segmenters were evaluated, which had their performance assessed on two data sets comprised of 18 mouse WSIs and 30 human WSIs. The results found corroborate our hypothesis validation.