PrPSeg：用于全景肾脏病理分割的通用命题学习
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
AI总结：
提出PrPSeg全景肾脏病理分割方法，通过通用命题矩阵、基于token的动态头网络架构和解剖损失函数，整合肾脏解剖学知识与空间关系，实现肾脏全景结构分割。
摘要：
理解肾脏病理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级的各种组成部分，包括区域（皮质、髓质）、功能单位（肾小球、肾小管）和细胞（肾小球内的足细胞、系膜细胞）。以往的研究主要忽略了临床知识中各对象之间复杂的空间相互关系。在本研究中，我们引入了一种名为全景肾脏病理分割（PrPSeg）的新型通用命题学习方法，旨在通过整合丰富的肾脏解剖学知识，全面分割肾脏内的全景结构。本文提出：（1）为肾脏病理设计一种综合的通用命题矩阵，促进将分类和空间关系纳入分割过程；（2）一种基于token的动态头单网络架构，改善了部分标签图像分割并具备未来数据扩展的能力；（3）一种解剖损失函数，用于量化肾脏中各对象间的相互关系。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
