开发跨平台工具，147倍提速肾小球足细胞分析
AMAP-APP: Efficient Segmentation and Morphometry Quantification of Fluorescent Microscopy Images of Podocytes
- 用经典图像处理替代密集分割，提升效率
- 处理速度提升147倍，结果与原方法高度一致（r>0.90）
- 支持多系统、易用界面，适合临床与科研人员
背景：自动化足细胞足突量化对肾脏研究至关重要，但现有的‘足细胞形态学自动分析’（AMAP）方法受限于高计算需求、缺乏用户界面及仅支持Linux系统。为此，我们开发了AMAP-APP，一款跨平台桌面应用以克服这些障碍。方法：AMAP-APP通过用经典图像处理替代高耗能的实例分割，同时保留原始语义分割模型来优化效率；引入改进的感兴趣区域（ROI）算法以提高精度。验证使用365张小鼠和人类样本图像（包含STED和共聚焦成像），通过皮尔逊相关系数和双单侧t检验（TOST）对比原方法性能。结果：在消费级硬件上，处理速度提升147倍；形态学输出（面积、周长、圆形度、裂孔隔膜密度）与原方法具有高度相关性（r>0.90）且统计等效（TOST P<0.05）。此外，新ROI算法相比原方法更准确，偏差更小。结论：AMAP-APP使基于深度学习的足细胞形态计量普及化。无需高性能计算集群，提供适用于Windows、macOS和Linux的友好界面，推动其在肾病研究乃至临床诊断中的广泛应用。
原文摘要 · Abstract (English)
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.
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