Abstract
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.
Abstract (translated)
背景：自动足突定量在肾脏研究中至关重要，但现有的“自动足突形态学分析”（AMAP）方法由于计算需求高、缺乏用户界面以及依赖Linux系统而受到限制。我们开发了AMAP-APP，这是一个跨平台的桌面应用程序，旨在克服这些障碍。 **方法**：AMAP-APP通过用传统的图像处理技术替代资源密集型实例分割，并保留原有的语义分割模型来提高效率。它还引入了一种改进的兴趣区域（ROI）算法以提高精度。验证过程包括使用365张来自小鼠和人类的STED和共聚焦图像，与原始AMAP方法进行皮尔逊相关性和双侧单边t检验（TOST）性能对比。 **结果**：在消费者硬件上，AMAP-APP实现了147倍的速度提升。形态学输出参数（面积、周长、圆度及裂隙隔膜密度）显示出与原始方法的高度相关性（r>0.90）和统计等效性（TOST P<0.05）。此外，新的ROI算法在精度方面优于原始版本，显示了其结果更接近于手动描绘。 **结论**：AMAP-APP使基于深度学习的足突形态学分析更加普及化。通过消除对高性能计算集群的需求，并为Windows、macOS和Linux系统提供用户友好的界面，它能够在肾病学研究中得到广泛应用，并具有潜在的临床诊断价值。
URL
https://arxiv.org/abs/2602.10663
