Go from assay to insights quickly and reliably with ImageXpress systems and IN Carta Image Analysis Software
IN Carta software makes it simple to embrace the complexity in an image. Deriving insights from 2D, 3D, and time-lapse data is streamlined by combining powerful analytics with a modern user interface. Machine learning technology and guided workflows create an optimal user experience where advanced phenotypic analysis is intuitive and the results are reliable. No need for image analysis expertise or tedious tweaking and testing of experimental parameters. Let IN Carta software do the heavy lifting so you can focus on the research. The answers can be in plain sight depending on how you look.
Guided workflows and scalable batch processing increase productivity. No time wasted with setting up the analysis and computation power to quickly deliver results.
Machine learning allows you to leverage more information and reduce error in the analysis of high-content screening data to enable new discoveries with confidence.
Simple by design
Intuitive user experience and cutting edge technology minimizes the software learning curve and removes barriers to productivity.
IN Carta Image Analysis Software
Browse to a parent directory and populate your Worklist with all available image data sets.
Analyze multiple experiments in batch analysis mode with one or more analysis protocols.
Browse and review images from experiments, create image analysis protocols, process data, and visualize analysis results.
Monitor the status of all submitted batch analysis tasks and oversee their progression in real-time.
Segment and quantify biological structures in true 3D space. IN Carta VoluMetrics provides algorithms that operate on 3D voxels when segmenting objects and extracting informative measures.
Machine learning, AI
Phenoglyphs is a machine learning classification algorithm that will analyze multiple measures simultaneously to automatically identify classes within the data and reveal deeper information than can be discovered with manual classification.
IN Carta VoluMetrics
3D cell culture models that more accurately mimic in vivo organs and tissues show great potential as tools to improve the understanding of disease and probe for potential therapies. IN Carta VoluMetrics is a 3D image analysis module that extends IN Carta’s functionality with the ability to segment and quantify biological structures in true 3D space.
IN Carta VoluMetrics provides algorithms that operate on 3D voxels when segmenting objects and extracting informative measures. This provides a better representation of the sample morphology and intensity distributions compared to individual z-plane analysis where the relationship between objects in adjacent z-planes is a rough approximation.
Randomly color segmentation masks to assess segmentation quality of touching objects in densely packed spheroids
IN Carta SINAP
SINAP is a module that uses deep learning algorithms to improve accuracy and reliability of high-content screening assays at the first step in the analysis pipeline, segmentation. With better object detection and preservation of the true morphology of objects of interest, quantitative information extracted at this step is free of error that misrepresents the biology in downstream analysis steps. With SINAP, Segmentation Is Not A Problem!
- Reliable – deep learning can account for high phenotypic variability and maintain accuracy across treatments even when objects are highly confluent or signal-to-noise is low
- Flexible – a single workflow can deal with a variety of applications and types of image data including brightfield
- Accessible – the algorithm learns to segment from users drawing on the image rather than asking the user to develop an image processing pipeline and optimize parameters
Multiplex cytological profiling assay to measure diverse cellular states.
IN Carta Phenoglyphs
Phenoglyphs is a phenotypic classifier that uses machine learning to take full advantage of all the content in an image. Hundreds of cellular features can be analyzed simultaneously to generate a more comprehensive phenotypic profile. This multi-variate approach to classification provides a better characterization of the individual cell populations which allows users to resolve even subtle phenotypic changes induced by a drug treatment or genetic modification.
- Comprehensive – a data driven approach that starts with an unsupervised clustering to find natural patterns in the data and highlight subpopulations without priori knowledge of what phenotypes may exist.
- Robust – the novel unsupervised step in the workflow quickly builds a large unbiased training set that captures the variance in a class and produces models that are less subject to overfitting and misclassifying cells.
- Simplified Workflow – choosing the optimal set of descriptive features and forming the complex set of rules to stratify classes is completely automated with machine learning. Optimal classification is achieved by simply confirming or correcting the algorithms predictions until it learns the right behavior.
The answers are hiding in plain sight when you change the way you look
Train the software by example. Machine learning and statistics will take care of the rest
ImageXpress Confocal HT.ai system
Powerful multi-laser light sources, a deep tissue penetrating confocal disk module, water immersion objectives and modern machine learning analysis software
- Ideal for highly-complex cell-based and 3D assays
- Seven-channel high-intensity lasers generating brighter images with higher signal-to-background
- Spinning confocal disk technology for deeper tissue penetration, resulting in sharper images with improved resolution
- Water immersion objectives offering quadruple the signal at lower exposure times for greater sensitivity and image clarity without sacrificing speed
- Optional IN Carta software, leveraging intuitive, modern machine learning
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