Automated Reference Kidney Histomorphometry using a Panoptic Segmentation Neural Network Correlates to Patient Demographics and Creatinine.
Automated Reference Kidney Histomorphometry using a Panoptic Segmentation Neural Network Correlates to Patient Demographics and Creatinine.
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使用全景分割神经网络的自动参考肾脏组织形态测量与患者人口统计数据和肌酐相关。
DOI：
10.1117/12.2655288
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Sarder,Pinaki
中科院分区：
文献类型：
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作者：
Ginley,Brandon;Lucarelli,Nicholas;Zee,Jarcy;Jain,Sanjay;Han,SeungSeok;Rodrigues,Luis;Wong,MichelleL;Jen,Kuang-Yu;Sarder,Pinaki
由于定量要求繁琐，缺乏健康人类肾脏的参考组织形态学数据。我们利用深度学习在一组多国参考肾组织切片中研究组织形态计量学与患者年龄、性别和血清肌酐的关系。开发了全景分割神经网络，并用于分割 79 个过碘酸希夫 (PAS) 染色的人类肾切除切片的数字化图像中的活肾小球、硬化肾小球、皮质和髓质间质、肾小管和动脉/小动脉，显示最小的病理变化。从分段类别中测量简单的形态测量（例如面积、半径、密度）。采用回归分析确定组织形态参数与年龄、性别和血清肌酐的关系。该模型对所有测试室均实现了较高的分割性能。我们发现，健康人的肾单位、动脉/小动脉的大小和密度以及间质的基线水平存在显着差异，来自不同地理位置的受试者之间可能存在很大差异。肾脏任何区域的肾单位大小显着取决于患者的肌酐。性别之间观察到肾血管系统和间质的轻微差异。最后，随着年龄的增长，肾小球硬化百分比增加，动脉/小动脉的皮质密度减少。我们表明，肾脏组织形态参数的精确测量可以自动化。即使在病理变化最小的参考肾组织切片中，一些组织形态学参数也显示出与患者人口统计数据和血清肌酐显着相关。这些强大的工具支持深度学习的可行性，以提高组织形态计量分析的效率和严谨性，并为未来的大规模研究铺平道路。
Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections.A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine.The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age.We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.