Deep Learning Approaches for mice glomeruli segmentation

Deep learning (DL) is widely applied in biomedical image processing nowadays. In this paper, we propose the use of DL architectures for glomerulus segmentation in histopathological images of mouse kidneys. Indeed, in humans, the analysis of the glomeruli is fundamental to decide on the transplantability of the organ. However, no datasets with human samples are publicly available. Therefore, obtaining good segmentation performance on the kidneys of mice could be the first step for a transfer learning approach to humans. We compared the use of two well-known architectures for image segmentation, namely MobileNet and DeepLab V2. Both models showed very promising results. © 2022 ESANN 2022 Proceedings - 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.