Muhao Liu, Chenyang Qi, Shunxing Bao, Quan Liu, Ruining Deng, Yu Wang, Shilin Zhao, Haichun Yang, and Yuankai Huo. “Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers.” Proceedings of SPIE Medical Imaging 2024: Digital and Computational Pathology, vol. 12933, 129330I, 2024.
Segmenting different layers of kidney structures, like the cortex and medulla, is crucial for analyzing kidney pathology images. Currently, this process is done manually, which is time-consuming and impractical for large-scale digital images. To address this, researchers have tested various deep learning methods to automate the segmentation of kidney layers. They evaluated several advanced models, including CNNs and Transformer-based models, using kidney images from mice. The results show that Transformer models generally perform better than CNN-based models, with a good Mean Intersection over Union (mIoU) score, indicating high accuracy. These findings suggest that deep learning can significantly improve the efficiency and accuracy of kidney layer segmentation in medical pathology.