PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
This work addresses the challenge of detailed renal pathology segmentation for medical diagnostics and research, representing an incremental advance by incorporating spatial relationships into existing segmentation frameworks.
The paper tackles the problem of segmenting complex kidney structures in pathology images by introducing PrPSeg, a universal proposition learning approach that integrates anatomical knowledge, resulting in improved segmentation accuracy with concrete metrics like a 5.2% increase in Dice score over prior methods.
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.