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[bibtex]@InProceedings{Deng_2024_CVPR, author = {Deng, Ruining and Liu, Quan and Cui, Can and Yao, Tianyuan and Yue, Jialin and Xiong, Juming and Yu, Lining and Wu, Yifei and Yin, Mengmeng and Wang, Yu and Zhao, Shilin and Tang, Yucheng and Yang, Haichun and Huo, Yuankai}, title = {PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {11736-11746} }
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
Abstract
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.
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