results: 研究发现，Auto-ICell系统可以实现高速、高效、自动化的单元细胞分析，并且可以评估单元细胞形态和 apoptosis 的分布。Abstract
The Auto-ICell system, a novel, and cost-effective integrated droplet microfluidic system, is introduced for real-time analysis of single-cell morphology and apoptosis. This system integrates a 3D-printed microfluidic chip with image analysis algorithms, enabling the generation of uniform droplet reactors and immediate image analysis. The system employs a color-based image analysis algorithm in the bright field for droplet content analysis. Meanwhile, in the fluorescence field, cell apoptosis is quantitatively measured through a combination of deep-learning-enabled multiple fluorescent channel analysis and a live/dead cell stain kit. Breast cancer cells are encapsulated within uniform droplets, with diameters ranging from 70 {\mu}m to 240 {\mu}m, generated at a high throughput of 1,500 droplets per minute. Real-time image analysis results are displayed within 2 seconds on a custom graphical user interface (GUI). The system provides an automatic calculation of the distribution and ratio of encapsulated dyes in the bright field, and in the fluorescent field, cell blebbing and cell circularity are observed and quantified respectively. The Auto-ICell system is non-invasive and provides online detection, offering a robust, time-efficient, user-friendly, and cost-effective solution for single-cell analysis. It significantly enhances the detection throughput of droplet single-cell analysis by reducing setup costs and improving operational performance. This study highlights the potential of the Auto-ICell system in advancing biological research and personalized disease treatment, with promising applications in cell culture, biochemical microreactors, drug carriers, cell-based assays, synthetic biology, and point-of-care diagnostics.
摘要
《Auto-ICell系统》是一种新型、成本效果的集成液态微机系统，用于实时分析单元细胞形态和 apoptosis。该系统结合了3D打印微机器件和图像分析算法，实现了生成固定尺寸液态室和即时图像分析。系统使用了色彩基于图像分析算法，在亮场进行液态内容分析。而在荧光场中，通过组合深度学习enabled多色渠道分析和live/dead细胞染料盒，量化细胞 apoptosis。具体来说，用 breast cancer细胞 encapsulated within uniform droplets，尺寸在70μm到240μm之间，通过高速生成1500个液态每分钟。实时图像分析结果在2秒钟内显示在自定义图形用户界面（GUI）上。系统提供了液态内容的自动计算和荧光场中细胞弹性和细胞圆形的观察和量化。Auto-ICell系统是不侵入的，提供了在线检测，为单元细胞分析提供了robust、时间高效、易用、成本效果的解决方案。它显著提高了液态单元细胞分析的设置成本和运行性能，有潜在应用于细胞文化、生化微 реактор、药物输送、细胞基因组分析、生物学synthesis和点检查诊断。
An invariant feature extraction for multi-modal images matching
results: 实验结果表明，该算法在实际数据上具有良好的普适性和准确性，能够实现多模式图像的准确空间对齐，表明了实际应用价值和良好的泛化能力。Abstract
This paper aims at providing an effective multi-modal images invariant feature extraction and matching algorithm for the application of multi-source data analysis. Focusing on the differences and correlation of multi-modal images, a feature-based matching algorithm is implemented. The key technologies include phase congruency (PC) and Shi-Tomasi feature point for keypoints detection, LogGabor filter and a weighted partial main orientation map (WPMOM) for feature extraction, and a multi-scale process to deal with scale differences and optimize matching results. The experimental results on practical data from multiple sources prove that the algorithm has effective performances on multi-modal images, which achieves accurate spatial alignment, showing practical application value and good generalization.
摘要
本文提出了一种可靠的多模态图像不变性特征提取和匹配算法，用于多源数据分析的应用。关注多模态图像之间的差异和相关性，实现了特征基于匹配算法。关键技术包括相位同步（PC）和史提莫asi特征点检测、LogGabor滤波器和权重部分主orientation图（WPMOM）特征提取，以及多尺度处理来处理比例差异和优化匹配结果。实验结果表明，该算法在实际数据上具有良好的 espacial 对齐性，达到了实际应用价值和好的泛化性。