Medical image analysis:
methods, use cases and AI technologies

Medical image analysis: methods, use cases and AI technologies

by Itransition Editorial Team

Medical image analysis is a process of extracting and refining meaningful information from medical images (CT scans, MRI, X-Rays, ultrasound, microscopic images). Our team automates medical image analysis, enhances its precision, and reduces processing time by delivering custom AI-driven software.

Key medical image analysis software stats

expected CAGR of medical image software market by 2028

Infinium Global Research

expected worth of AI-based medical image analysis software by 2026

Definitive Healthcare

more skin cancer cases can be detected by AI than human professionals


Medical image analysis: services we offer

25+ years of experience in the healthcare industry, combined with in-depth expertise in AI/ML, enable our team to offer a wide range of services for medical image analysis.

Audit and consulting

  • We help healthcare practitioners choose the type of medical image analysis software that suits their objectives.
  • We ensure an organization's medical image analysis solution complies with the necessary industrial and government regulations.
  • Our experts monitor the performance and security of medical image analysis software and give recommendations regarding the solution’s safeguarding and optimization.
Our solutions


Cross-platform compatible

Easy to integrate

Optimized for mobile



AI technologies and tools for image analysis

Our experts have a robust technical portfolio, which enables them to employ the AI algorithms best suited for particular medical image analysis cases. The most commonly selected technologies include:

AI technologies and tools for image analysis
  • CNN (Convolutional neural networks)
  • RNN (Recurrent neural networks)
  • GAN (Generative adversarial networks)
  • Autoencoders
  • TensorFlow
  • PyTorch
  • OpenCV
  • Pillow
  • NumPy
  • SciPy
  • Scikit-image
  • Clusterization: DSCAN, k-means
  • Similarity: Annoy, Faiss
  • Reduce dimension: ICA, t-SNE, etc.

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Applying deep learning for medical image analysis

Increased sophistication of artificial intelligence and implementation of deep learning algorithms in medical image analysis increase its accuracy. Different solutions may use varying deep learning patterns depending on the medical area.

Scheme title: Deep learning in medical image analysis
Data source: — Going deep in medical image analysis: concepts, methods, challenges, and future directions

Deep learning in medical image analysis
Pattern recognition task
Anatomical region

Detection/ Localization

Helps determine whether the objects of a specific class (e.g., tumors) are present in the studied area and then localize their exact coordinates (including localizing them in a 3D model based on the 2D images).

Image segmentation

Used to recognize contours of particular organs or anatomical structures in an image and to discover any anomalies in organ structures (traumas, lesions, etc.).


Aligns two or more images to study the correspondence among images taken at different times, with different imaging devices, from varying viewpoints, or even from multiple patients.


Helps separate medical images that contain anomalies from the ones that don’t. Widely used in all types of cancer detection, dermatological and ophthalmological diseases recognition.


Transforms the original image, enhancing it, making it clearer, changing viewpoint, or even adding another dimension to it in order to present original data in a new and more comprehensive way.

Clinical use cases

Medical image processing is employed for almost all systems of organs and body regions to enhance the quality of diagnostics, treatment planning, and research of dangerous conditions.


Brain Bone marrow

Lymph nodes Chest/Breast

Abdomen Prostate/testicular/ ovarial

Skin Metastasis


Bone fractures

Intracranial hemorrhage detection and localization

Intracranial aneurysm and stroke detection

Organ ruptures and hemorrhages

Lesions and fibrosis

Breast Bones Liver

Pancreas Spleen

Kidneys Lungs

Prostate/ovaries Other organs

Infection/ inflammation

Localization and identification of thoracic diseases

Retinal diseases Joints

Lymph nodes Myocardium

Muscles Soft tissue organs

Organs’ misplacement or malfunction

Identification of abnormal position of any organ

Macular edema (DME) and diabetic retinopathy (DR) detection

Age-related macular degeneration (AMD) detection


Coronary artery calcium and plaque detection

Detection of chronic obstructive pulmonary disease (COPD) and acute respiratory disease (ARD)

Segmentation of cardiac and blood vessel structures

Top 5 medical imaging methods

Medical image data usually comes from one of the five most popular medical imaging methods. We build medical image analysis software compatible with all of them.


Uses X-rays to detect anomalies (fractures, free air, tumors, calcites, free fluids, inflammation, and foreign objects) in bones and other dense organs.
Quick, non-invasive, and painless. It can help diagnose various diseases and injuries, including broken bones, some types of cancers, and infections.
Slight carcinogenic effect due to ionizing radiation. The risk is greater for children, and the procedure is not recommended for pregnant patients.

Medical image file formats

Our solutions are compatible with all of the popular medical image file formats.

File formats








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Capabilities of medical image analysis

Medical image processing supplies healthcare professionals with unique data crucial for accurate diagnostics and treatment of all conditions.

Improved diagnostics
Optimized treatment
Enhanced patient safety
Increased productivity for healthcare specialists
Quicker and safer drug development
More favorable health outcomes
Facilitated medical personnel training
Boosted medical research

Challenges of medical image analysis

Our experts continually improve the quality of our medical image processing solutions while keeping ahead of the common industry challenges.


Possible solution


Small-scale medical datasets


Possible solution

Adapt a model trained on regular images to medical images or from one modality to another. Perform collaborative training among multiple data centers. Employ publicly available benchmark datasets.


Lack of annotated and balanced data


Possible solution

Use data augmentation techniques and GANs for synthetic image generation, combine deep learning and multitask learning models, or wrap deep features for medical image analysis.


The "black box" effect: lack of algorithm explanation


Possible solution

Use visualization for deep learning models to give the designated users an idea of how the algorithm works, providing a way to correct flaws in the logical chains.


Pushback from some healthcare professionals


Possible solution

Educate medical personnel on the specifics and usage of the medical image analysis software, explain why it is not undermining their expertise, and show the opportunities that technology opens for healthcare specialists.

AI-enabled image analysis: solution roadmap

Working on your AI-driven system, we will follow a straightforward three-step process to create a foundation for advanced data operations. In turn, it will help to launch solutions in real-life business or scientific settings.



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



Before training, we clean up and label your data. Then we build a model by training specific algorithms with prepared datasets, which include raw structured and unstructured data generated by humans and machines.



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

Medical image analysis FAQ

What is DICOM, and why is it used for medical imaging?

Is image analysis the same as image processing?

What medical devices are used for medical imaging?

Why is machine learning used in medical image analysis?

Does medical image analysis software have limitations?