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E-mail
info.microscopy.cn@zeiss.com
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Phone
13761758023
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Address
No. 60 Meiyue Road, Pudong New Area Free Trade Zone, Shanghai
Carl Zeiss (Shanghai) Management Co., Ltd
info.microscopy.cn@zeiss.com
13761758023
No. 60 Meiyue Road, Pudong New Area Free Trade Zone, Shanghai
Carl Zeiss Arivis
Zeiss Arivis helps you achieve scalable automated image analysis processes on the local, VR, server, and cloud platforms through its four products: Arivis Pro, Rivis Pro VR, Arivis Hub, and Arivis Cloud. With the help of traditional methods or artificial intelligence models, it is easy to create image analysis processes suitable for 2D to 5D. This software supports and is proficient in processing over 60 file formats, making it easy and efficient to complete analysis tasks even when dealing with images of TB size. At the same time, the software can provide preset analysis processes and customized workflows for specific purposes. With just one click, you can easily obtain batch results, helping you balance analysis efficiency and throughput.
Arivis Pro: a powerful, flexible, efficient, and open compatible interactive advanced image intelligent analysis software that is fully compatible with various image sources, formats, and sizes, making it easy to handle complex and arduous image analysis and visualization tasks
Arivis Pro VR: a VR version that can be used in conjunction with Pro, allowing for immersive exploration and editing of data, including cell tracking, neural tracking, and 3D annotation
Arivis Hub: a server version dedicated to data management that can be used in conjunction with Pro. It can upload images in real-time and batch process them with just one click, making it an ideal choice for 3D high content analysischoice
Arivis Cloud: a cloud version dedicated to Al training that can be used in conjunction with Pro, iPad、 Both mobile phones can be used
TBData analysis:
· 3D rendering and quantitative analysis of large-scale image data up to TB level
· Low dependence on memory and RAM, big data is not afraid of crashes and crashes
· Small PC computing power consumption, multiple data can be processed simultaneously without affecting the use of other software
· CZI images do not require format conversion, even TB level images can be instantly opened
AIEasy to use and efficient:
· AI full process: AI denoising, AI labeling, AI segmentation, AI classification
· Rich AI segmentation methods to handle images of different difficulty levels: machine learning, deep learning (including the latest Cellpose, Semantic, Instance models)
· An AI training cloud platform that enables interoperability between local and mobile devices
· Local AI training effect can be previewed
Flexible and scalable:
· specialPipeline mode, flexible and easy to use, multi terminal interoperability
· Multiple modules to meet various needs: cell tracking and dynamic tracking, neural tracking and dendritic spine tracking, high content modules, multiple analysis tools (quantitative analysis, compartment analysis, distance analysis, co localization analysis), Volume Fusion multimodal fusion module, preprocessing module
·The high connotation module supports visualized orifice plate batch processing, real-time display of progress, support for abnormal hole re inspection, and built-in hundreds of professional analysis parameters
· Python compilation mode without the need for third-party plugins, facilitating personalized image analysis with multiple scripts to choose from, such as brain map registration, Sholl analysis, microtubule analysis, etc
· Seamless connection between local end, cloud end, server end, and VR end
During the embryonic development of fruit flies, the Zeiss Arivis Pro tracking tool is used to dynamically track and display the trajectory of cells during the development process
Automated 3D imaging of HeLa cells was performed using Zeiss dual beam electron microscopy Crossbeam series FIB-SEM, and subsequent data segmentation was performed using Zeiss Arivis Pro with Zeiss Arivis Cloud deep learning

Extracting NCM particles from the positive electrode of lithium batteries using ARIVIS deep learning segmentation, image source Markus B ö se
