Uni-AIMS: AI-Powered Microscopy Image Analysis

2025-05-11

AI Summary

To address challenges in microscopic imaging—including dense small-object arrangements, large scale variations, and scarce annotations—this paper proposes an intelligent analysis system tailored for microscopy. Methodologically, we design a dedicated instance segmentation network enabling robust separation of up to thousands of tightly packed objects; develop a human-in-the-loop data engine integrating real acquisition, controllable synthetic generation, and closed-loop annotation to alleviate labeling bottlenecks; and incorporate multi-source data fusion with an OCR-based scale bar recognition module for automatic scale calibration. Our key contributions are the first microscopy-specific instance segmentation paradigm and a scalable, synergistic data generation framework. Evaluated on diverse biological and materials microscopy datasets, the system achieves a mean average precision (mAP) of 92.3% for segmentation and 98.7% accuracy for scale bar recognition. It has been successfully deployed and is operating stably across three interdisciplinary experimental platforms.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Intelligent recognition of microscopy images
• Robust segmentation of small and large objects
• Automatic scale bar recognition for quantitative analysis

Innovation

• Data engine generates annotated microscopy datasets
• Segmentation model detects small and large objects
• Automatic recognition of image scale bars