Evaluation Kidney Layer Segmentation on Whole Slide Imaging using
  Convolutional Neural Networks and Transformers
Muhao Liu Chenyang Qi Shunxing Bao Quan Liu Ruining Deng Yu Wang Shilin Zhao Haichun Yang Yuankai Huo
The segmentation of kidney layer structures, including cortex, outer stripe,
inner stripe, and inner medulla within human kidney whole slide images (WSI)
plays an essential role in automated image analysis in renal pathology.
However, the current manual segmentation process proves labor-intensive and
infeasible for handling the extensive digital pathology images encountered at a
large scale. In response, the realm of digital renal pathology has seen the
emergence of deep learning-based methodologies. However, very few, if any, deep
learning based approaches have been applied to kidney layer structure
segmentation. Addressing this gap, this paper assesses the feasibility of
performing deep learning based approaches on kidney layer structure
segmetnation. This study employs the representative convolutional neural
network (CNN) and Transformer segmentation approaches, including Swin-Unet,
Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We
quantitatively evaluated six prevalent deep learning models on renal cortex
layer segmentation using mice kidney WSIs. The empirical results stemming from
our approach exhibit compelling advancements, as evidenced by a decent Mean
Intersection over Union (mIoU) index. The results demonstrate that Transformer
models generally outperform CNN-based models. By enabling a quantitative
evaluation of renal cortical structures, deep learning approaches are promising
to empower these medical professionals to make more informed kidney layer
segmentation.