  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.
