CellPathfinder

Through the use of machine learning algorithms, this software is capable of recognizing and analyzing patterns in images of unlabeled cells taken with bright-field microscopes. Furthermore, by using the new pattern recognition function with these images, it is possible to accurately analyze changes in cell proliferation that occur over time. This software also has a 3D image analysis function for the observation of cells  that are being grown under 3D-conditions similar to a natural environment. This function makes it easy to quantify cell aggregate volume, surface area, and location. CellPathfinder can also be used with Yokogawa’s CQ1 confocal quantitative image cytometer for the high-precision quantification of cells.

CellPathfinder supports the image data acquired by Yokogawa imaging systems CQ1 and CellVoyager.

  • Bench top size confocal system
  • Simple operation and automated image acquisition of many sample
  • Live cell imaging
  • Ultimate HCA system for high-quality imaging and high-throughput screening with water immersion objectives and multiple cameras
  • Built-in robot pipetter for kinetic assays

※Data acquired in CellVoyager CV1000 are not supported.

※CellPathfinder system contain the software and the workstation.

Simple workflow from images to analysis and graphs

 

1. Display image data

 
 

2. Load and execute analysis protocol

 

・Easy-to-understand graphical icons
・Choose a preset template for your analysis

3. Gating

・ Specific populations can be extracted by gating the feature value data of recognized objects.

・The extracted populations can be analyzed further.

4. Make the graphs

・Various graph options to visualize the results
・The link between graph and images enables quick visual check of images by clicking data points

5. To examine further details…list the profiles of interesting cells

・Images and numerical data can be collected by clicking cells.

 

・Basic and Advance analysis modes are implemented.
・Beginners can perform quantitative analysis in Basic mode.
・Experts can flexibly customize the analysis in Advance mode.

◆3D analysis
・Analysis of Z-stack images in three-dimensional space.
・The volume and the location of objects in 3D space can be quantified.

 

◆Recognition of cytoskeleton by machine learning
・Software learns the features of the sample objects to analyze the images.


 

◆Label-free analysis
・Machine learning algorithm can recognize nuclei and cell bodies in unstained bright field images.
 

System configuration

・Software
・Workstation
・Displays

Specifications of the workstation
Model: Dell Precision T5810XL
CPU: Intel® Xeon® E5-1620 v4
Memory:128 GB
HDD: System(C:) 4TB Storage, (D:) 4TB
OS: Windows® 10 Pro 64 bit Japanese/English
Display: 1920×1200, dual monitor

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