Image Analysis and Artificial Intelligence

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Image Analysis and Artificial Intelligence



Machine-based image analysis offers enhanced reproducibility, accuracy, and high speed. At Axion BioSystems, we develop and apply artificial intelligence and machine learning to enhance and facilitate life science applications. Modules for image recognition of cells and organoids are designed for the analysis and modelling of complex biological processes. This allows scientists to make data-driven decisions and generate robust and reproducible results.


What is AI-powered image analysis?

Artificial Intelligence (AI)-driven analysis represents an indispensable approach to manage increasingly growing (in size and complexity) microscopy datasets. Since visual analysis of microscopy data can be operator-dependent, subjective, and, at times, lacking reproducibility, it is crucial to move towards more objective and quantitative processes. Image analysis comprises computerized extraction of relevant information from digital images. A variety of AI-based tools have been developed to automate and improve biomedical image analysis.

Artificial intelligence techniques can be broadly subdivided into formal reasoning techniques based on knowledge and explicit rules and machine learning-based techniques, which rest upon learning from data patterns. Deep learning is a machine-learning technique that relies on multilayered artificial neural networks. Here, at Axion BioSystems, we utilize a deep learning-driven, rather than knowledge-driven, image analysis.


Axion BioSystems Image Processing Pipeline

The machine learning-mediated approach that we utilize to develop tools for bioimage analysis consists of the following steps:

>> Data input

>> Data labelling

>> Model training

>> Evaluation of performance

>> Release of the model to customers


To start with, our input data gets labelled by experienced image processing engineers. Subsequently, high-performance models are trained using large volumes of labelled image data. Following evaluation of model’s performance (and if needed, model retraining), the model gets released to the customers for their use.