PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

TL;DR

PrPSeg introduces a universal proposition learning framework that integrates extensive kidney anatomy knowledge to improve panoramic renal pathology segmentation, addressing complex spatial relationships and enabling comprehensive analysis.

Contribution

It proposes a novel universal proposition matrix, a token-based dynamic head network, and an anatomy loss function for enhanced renal pathology segmentation.

Findings

Effective segmentation of panoramic kidney structures.

Improved handling of partial labels and future data expansion.

Quantification of inter-object relationships in kidney anatomy.

Abstract

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various components across multiple levels, including regions (cortex, medulla), functional units (glomeruli, tubules), and cells (podocytes, mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research, we introduce a novel universal proposition learning approach, called panoramic renal pathology segmentation (PrPSeg), designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper, we propose (1) the design of a comprehensive universal proposition matrix for renal pathology, facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture, with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function, quantifying the inter-object relationships across the kidney.