Uni-AIMS: AI-Powered Microscopy Image Analysis

• URL: http://arxiv.org/abs/2505.06918v1
• Date: Sun, 11 May 2025 09:35:53 GMT
• Title: Uni-AIMS: AI-Powered Microscopy Image Analysis
• Authors: Yanhui Hong, Nan Wang, Zhiyi Xia, Haoyi Tao, Xi Fang, Yiming Li, Jiankun Wang, Peng Jin, Xiaochen Cai, Shengyu Li, Ziqi Chen, Zezhong Zhang, Guolin Ke, Linfeng Zhang,
• Abstract summary: We develop a data engine that generates high-quality annotated datasets.<n>We propose a segmentation model capable of robustly detecting both small and large objects.<n>Our solution supports the precise automatic recognition of image scale bars.
• Score: 24.917584553189187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a systematic solution for the intelligent recognition and automatic analysis of microscopy images. We developed a data engine that generates high-quality annotated datasets through a combination of the collection of diverse microscopy images from experiments, synthetic data generation and a human-in-the-loop annotation process. To address the unique challenges of microscopy images, we propose a segmentation model capable of robustly detecting both small and large objects. The model effectively identifies and separates thousands of closely situated targets, even in cluttered visual environments. Furthermore, our solution supports the precise automatic recognition of image scale bars, an essential feature in quantitative microscopic analysis. Building upon these components, we have constructed a comprehensive intelligent analysis platform and validated its effectiveness and practicality in real-world applications. This study not only advances automatic recognition in microscopy imaging but also ensures scalability and generalizability across multiple application domains, offering a powerful tool for automated microscopic analysis in interdisciplinary research.

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