Background: Automated podocyte foot process quantification is vital for kidney research, but the established "Automatic Morphological Analysis of Podocytes" (AMAP) method is hindered by high computational demands, a lack of a user interface, and Linux dependency. We developed AMAP-APP, a cross-platform desktop application designed to overcome these barriers. Methods: AMAP-APP optimizes efficiency by replacing intensive instance segmentation with classic image processing while retaining the original semantic segmentation model. It introduces a refined Region of Interest (ROI) algorithm to improve precision. Validation involved 365 mouse and human images (STED and confocal), benchmarking performance against the original AMAP via Pearson correlation and Two One-Sided T-tests (TOST). Results: AMAP-APP achieved a 147-fold increase in processing speed on consumer hardware. Morphometric outputs (area, perimeter, circularity, and slit diaphragm density) showed high correlation (r>0.90) and statistical equivalence (TOST P<0.05) to the original method. Additionally, the new ROI algorithm demonstrated superior accuracy compared to the original, showing reduced deviation from manual delineations. Conclusion: AMAP-APP democratizes deep learning-based podocyte morphometry. By eliminating the need for high-performance computing clusters and providing a user-friendly interface for Windows, macOS, and Linux, it enables widespread adoption in nephrology research and potential clinical diagnostics.

翻译：背景：足细胞足突的自动化量化对于肾脏研究至关重要，但已建立的"足细胞自动形态学分析"（AMAP）方法受限于高计算需求、缺乏用户界面以及对Linux系统的依赖。我们开发了AMAP-APP，一个旨在克服这些障碍的跨平台桌面应用程序。方法：AMAP-APP通过用经典图像处理替代计算密集的实例分割来优化效率，同时保留了原有的语义分割模型。它引入了一种改进的感兴趣区域（ROI）算法以提高精度。验证过程涉及365张小鼠和人类图像（STED和共聚焦），通过皮尔逊相关系数和双单侧t检验（TOST）与原版AMAP进行性能基准测试。结果：AMAP-APP在消费级硬件上实现了147倍的处理速度提升。形态计量学输出（面积、周长、圆形度和裂孔隔膜密度）与原方法显示出高度相关性（r>0.90）和统计学等效性（TOST P<0.05）。此外，新的ROI算法相较于原算法展现出更高的准确性，与手动描绘的偏差更小。结论：AMAP-APP普及了基于深度学习的足细胞形态计量学。通过消除对高性能计算集群的需求，并为Windows、macOS和Linux提供用户友好的界面，它促进了该方法在肾脏病学研究和潜在临床诊断中的广泛应用。