# A Self-Adaptive Strip Pooling Network for Segmenting the Kidney Glomerular Basement Membrane

## TL;DR

A new self-adaptive network is proposed to accurately segment and measure the thickness of the kidney's glomerular basement membrane, aiding in pathological diagnosis.

## Contribution

The novel RSP model with edge attention and revised loss function improves GBM segmentation accuracy and completeness.

## Key findings

- The RSP model effectively captures strip and square features of the glomerular basement membrane.
- The edge attention mechanism and revised loss function enhance segmentation quality and alignment with surrounding tissues.
- The proposed method outperforms existing models in GBM segmentation and provides precise thickness measurements.

## Abstract

Accurate semantic segmentation and automatic thickness measurement of the glomerular basement membrane (GBM) can aid pathologists in carrying out subsequent pathological diagnoses. The GBM has a complex ultrastructure and irregular shape, which makes it difficult to segment accurately. We found that the shape of the GBM is striped, so we proposed an RSP model to extract both the strip and square features of the GBM. Additionally, grayscale images of the GBM are similar to those of surrounding tissues, and the contrast is low. We added an edge attention mechanism to further improve the quality of segmentation. Moreover, we revised the pixel-level loss function to consider the tissues around the GBM and locate the GBM as a doctor would, i.e., by using the tissues as the reference object. Ablation experiments with each module showed that SSPNet can better segment the GBM. The proposed method was also compared with the existing medical semantic segmentation model. The experimental results showed that the proposed method can obtain high-precision segmentation results for the GBM and completely segment the target. Finally, the thickness of the GBM was calculated using a skeleton extraction method to provide quantitative data for expert diagnosis.