Mouse-to-human transfer learning for glomerulus segmentation

Abstract

ABSTRACT Mice and humans share many features of internal organs. Therefore, mice are often used in experimental models of human diseases. Although this is commonplace in medicine, there is an avenue to go to explore it in computational pathology, where digital whole-slide images (WSIs) are the main objects of investigation. Considering the absence of research about knowledge transfer between mice and humans in machine learning modelling, we propose investigating the possibility of segmenting glomeruli in human WSIs by training deep learning models on mouse data only. A set of semantic segmenters were evaluated, which had their performance assessed on two data sets comprised of 18 mouse WSIs, and 42 human WSIs. Results demonstrated that U-Net 3+ achieved superior results on intra-data set: On the mouse data set, it reached the highest average score on HE-stained images, while on the human data set, this network achieved the highest average on all stains. U-Net 3+ also obtained the best results after being trained only on the mouse data set and predicting on the entire (train and test) human data set. Although all networks proved to be capable of segmenting intra-stain images, it was not possible to confirm the same results on inter-stain ones.