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.

翻译：理解肾脏病理的解剖结构对于推进疾病诊断、治疗评估和临床研究至关重要。复杂的肾脏系统包含多个层面上的多种组成部分，包括区域（皮质、髓质）、功能单元（肾小球、肾小管）和细胞（肾小球中的足细胞、系膜细胞）。以往研究大多忽略了从临床知识中获得的物体间复杂的空间相互关系。在本研究中，我们提出了一种新颖的通用命题学习方法——全景肾脏病理分割（PrPSeg），旨在通过整合肾脏解剖学的广泛知识，全面分割肾脏内的全景结构。本文提出了：(1) 设计肾脏病理的通用命题矩阵，将分类和空间关系融入分割过程；(2) 基于令牌的动态单网络架构，改进了部分标注图像的分割能力，并具备未来数据扩展的潜力；(3) 一种解剖损失函数，用于量化肾脏中物体间的相互关系。