Medical image analysis

Medical image analysis

Image analysis automation

We are ready to streamline manual image analysis with automation and added precision. Custom ML-driven image analysis software by Itransition offers a range of benefits for practitioners, researchers, and decision-makers:

Who we serve

How our solutions help

Care providers

Who we serve

How our solutions help

Facilitate early and non-invasive diagnosis Prevent collateral damage in surgeries Avoid repetitive imaging

Limit patient exposure Level up precision medicine Lower imaging cost

Research labs

Who we serve

How our solutions help

Streamline drug and treatment efficiency testing methods Check imaging biomarkers in trials

Foster informed decision-making

Looking to introduce intelligent image analysis to your medical workflows?

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Medical image analysis software: our offer

We create full-fledged computer vision solutions for healthcare-associated purposes that rely on deep learning to build highly capable neural networks. Well-trained and able to learn as it operates, medical image analysis software delivers a detailed analysis of a medical scan for further consideration and validation by medical professionals and researchers.

  • Vendor-neutral
  • Interoperable
  • Cross-platform compatible
  • Optimized for mobile

Image analysis techniques

To help you drive the quality of diagnostics and medical research, we build, adapt and train ML models, incorporating four techniques, integral to accurate image analysis:

Image pre-processing

We improve image quality by raising contrast and removing spatial gaps, noise, etc. After these manipulations, the image can serve as a base for diagnosing.

Segmentation and quantification

We apply segmentation to single out the needed body part and remove unrelated objects. The model will see the body part more clearly and reduce the number of faulty results. Quantification allows assigning attributes (object shape, size, form, etc.) to train the model to classify images correctly.


We combine images made in different modalities or timeframes. Due to fusion, the model can receive data from different sensors (e.g. those used in CT and MRI) and learn to deliver valid results.

Modalities & formats

Modalities & formats

Our solutions can process and analyze medical images of various modalities and formats, including 3D images:


Thermography CT PET

Echocardiography Ultrasound MRI, and more

Image formats


Interfile NIfTI RAW, and more

Modalities & formats

Discuss your project with Itransition’s data scientists and consultants directly

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Related services

Healthcare software

Healthcare software

Our experienced professionals have a full stack of technologies to provide solutions for healthcare professionals, patients, and researchers. Medical CRMs, mobile health software, programming solutions for medical equipment – we can do this and more.

Medical data analytics

We create custom and platform-based analytical tools to enable healthcare data analysis at diagnostic, predictive and preventive levels.

Computer vision

Itransition trains and deploys custom machine learning models to enable advanced image recognition, visual search, robotic vision, and more, integrating these capabilities into web, mobile and embedded platforms.

Machine learning

Machine learning

Our team takes part in ML projects for a range of industries, such as retail, finance, healthcare and more. We’ll consult you on the optimal solution and create software that meets your key business needs.

ML solution design roadmap

Working on your ML-driven system, we will follow this straightforward 3-step process to create your foundation for advanced data operations. This in turn will result in solutions to be applied in real-life business or scientific settings.

1 Preparation

With your requirements in mind, we create custom data scripts to be added to ready-made third-party ML models or to custom models we deliver according to your needs and specifications.

2 Training

Prior to training, we clean up and label your data. Then we train neural networks applying specific algorithms to your datasets that include raw structured and unstructured data generated by humans and machines.

3 Tuning

We monitor the results and refine the learning process until the results get satisfactory, i.e. neural networks stabilize and the developed software starts learning from its own mistakes without any human intervention.