\section{Introduction}
\label{sec:intro}

\begin{figure*}[t]
\centering 
\includegraphics[width=1.0\linewidth]{{fig/Tease.pdf}}
\caption{
\textbf{Knowledge transformation from kidney anatomy to computational modeling -- }
This figure demonstrates the transformation of intricate clinical anatomical relationships within the kidney into a structured computational matrix. (a) Pathologists examine histopathology following the kidney anatomy. (b) This study revisits such kidney anatomy with hierarchical semantic taxonomy. (c) The proposed PrPSeg method further mathematically abstracts the semantic taxonomy as a universal proposition matrix. This matrix serves as a foundation for our computational model, reflecting the complex interplay of anatomical elements in the kidney.} 
\label{Fig:tease} 
\end{figure*}

 %The digitization of glass slides using whole slide image (WSI) scanners, a process known as 
 Digital pathology has revolutionized the field of pathology~\cite{bandari2016renal}, not only facilitating the transition from local microscopes to remote monitoring for pathologists but also providing a significant opportunity for large-scale computer-assisted quantification in pathology~\cite{bengtsson2017computer, marti2021digital, gomes2021building}. In the clinical anatomy of renal pathology, there are different levels of quantification necessary for disease diagnosis~\cite{mounier2002cortical}, severity recognition~\cite{kellum2008acute}, and treatment effectiveness evaluation~\cite{jimenez2006mast}, ranging from region-level objects (like medulla and cortex) to functional units (glomerulus, tubules, vessels, etc.) and even to individual cells within these units. These tasks, especially at the functional unit and cell levels, are prone to errors and variability in human examination and require labor-intensive efforts~\cite{wijkstrom2018morphological, zheng2021deep, imig2022interactions}. Therefore, achieving comprehensive quantification from regions to cells is necessary in renal pathology, but it remains an inevitably laborious task with manual human effort.
 

While many studies have developed pathological image segmentation techniques for pixel-level tissue characterization, particularly using deep learning methods~\cite{kumar2017dataset, ding2020multi, ren2017computer, bel2018structure, zeng2020identification}, they still encounter three major limitations: (1) Current multi-network and multi-head designs~\cite{jayapandian2021development, li2019u, hermsen2019deep, zhang2021dodnet, wu2022tgnet, deng2023omni} focus only on single tissue structures or structures at similar scales, lacking a comprehensive approach to achieve all-encompassing segmentation across different levels from regions to cells. (2) These approaches require modifications to their architectures when new classes are introduced, preventing the reuse of existing backbones without significant alterations; (3) Comprehensive semantic (multi-label) segmentation and quantification on renal histopathological images remain challenging due to the intricate spatial relationships among different tissue structures. The spatial relationships between these different objects are illustrated in~\Cref{Fig:tease}.  \emph{Understanding these interrelations in renal pathology is crucial for achieving effective all-encompassing segmentation, yet recent advancements in deep learning have not fully incorporated this comprehensive modeling into the training process, nor have they achieved panoramic segmentation for the complete anatomy of the kidney.}


To address these challenges, we introduce a token-based dynamic head network designed to achieve panoramic renal pathology segmentation (PrPSeg) by modeling the spatial relationships among all objects. This approach allows for the reuse of the same architectural framework, even when the dataset size expands. A universal proposition matrix is established to translate anatomical relationships into computational modeling concepts. A anatomy loss is also proposed to integrate spatial relationships into the model training as a form of semi-supervised learning. To our knowledge, this is the first deep learning algorithm to accomplish panoramic segmentation in renal pathology, demonstrating superior performance in all-encompassing segmentation.


The contributions of this paper are threefold: 
\begin{itemize}
    \item The design of a comprehensive universal proposition matrix for renal pathology, facilitating the incorporation of classification and spatial relationships into the segmentation process.
    \item The development of a token-based dynamic head in a single network architecture, improving partial label image segmentation.
    \item The formulation of an anatomy loss function, quantifying the inter-object relationships across the kidney.

\end{itemize}