Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology

TL;DR

This paper demonstrates that incorporating cross-species homologous data, such as mouse kidney images, into training datasets improves the accuracy and generalization of deep learning models for kidney layer segmentation in humans.

Contribution

The study introduces a novel approach of using cross-species homologous data to enhance deep learning model performance in kidney segmentation tasks.

Findings

Model performance increased by up to 1.77% in mIoU.

Model generalization improved with cross-species data.

Cross-species data acts as a low-noise training source.

Abstract

Accurate delineation of the boundaries between the renal cortex and medulla is crucial for subsequent functional structural analysis and disease diagnosis. Training high-quality deep-learning models for layer segmentation relies on the availability of large amounts of annotated data. However, due to the patient's privacy of medical data and scarce clinical cases, constructing pathological datasets from clinical sources is relatively difficult and expensive. Moreover, using external natural image datasets introduces noise during the domain generalization process. Cross-species homologous data, such as mouse kidney data, which exhibits high structural and feature similarity to human kidneys, has the potential to enhance model performance on human datasets. In this study, we incorporated the collected private Periodic Acid-Schiff (PAS) stained mouse kidney dataset into the human kidney dataset for joint training. The results showed that after introducing cross-species homologous data, the semantic segmentation models based on CNN and Transformer architectures achieved an average increase of 1.77% and 1.24% in mIoU, and 1.76% and 0.89% in Dice score for the human renal cortex and medulla datasets, respectively. This approach is also capable of enhancing the model's generalization ability. This indicates that cross-species homologous data, as a low-noise trainable data source, can help improve model performance under conditions of limited clinical samples. Code is available at https://github.com/hrlblab/layer_segmentation.