Medical image analysis: methods, use cases and AI technologies
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
IDTechEx
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.
- 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.
Development
- We create medical computer vision solutions that rely on AI techniques including deep learning neural networks.
- Our image analysis software is well-trained and can learn as it operates.
- Our solutions deliver a detailed image analysis, including visualization and exploration of 2D images and 3D volumes, segmentation, classification, registration, and 3D reconstruction of image data, for further consideration and validation by medical professionals and researchers.
Vendor-neutral
Cross-platform compatible
Easy to integrate
Optimized for mobile
Secure
Reliable
Our solutions
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:
Deep learning architectures
- CNN (Convolutional neural networks)
- RNN (Recurrent neural networks)
- GAN (Generative adversarial networks)
- Autoencoders
Frameworks and libraries
- TensorFlow
- PyTorch
- OpenCV
- Pillow
- NumPy
- SciPy
- Scikit-image
Classic ML algorithms
- Clusterization: DSCAN, k-means
- Similarity: Annoy, Faiss
- Reduce dimension: ICA, t-SNE, etc.
Looking to introduce intelligent image analysis to your medical workflows?
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: ieeexplore.ieee.org — Going deep in medical image analysis: concepts, methods, challenges, and future directions
Deep learning in medical image analysis
Pattern recognition task
Detection/Localization
Segmentation
Registration
Classification
Anatomical region
Brain
Breast
Eye
Chest
Abdomen
Miscellaneous
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.).
Registration
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.
Classification
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.
Visualization
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.
Tumors
Brain Bone marrow
Lymph nodes Chest/Breast
Abdomen Prostate/testicular/ ovarial
Skin Metastasis
Traumas
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
Miscellaneous
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.
X-rays
Approach
Uses X-rays to detect anomalies (fractures, free air, tumors, calcites, free fluids, inflammation, and foreign objects) in bones and other dense organs.
Advantages
Quick, non-invasive, and painless.
It can help diagnose various diseases and injuries, including broken bones, some types of cancers, and infections.
Сautions
Slight carcinogenic effect due to ionizing radiation. The risk is greater for children, and the procedure is not recommended for pregnant patients.
Computed tomography scan
Approach
Combines a series of X-ray images taken from different angles around the body and uses digital processing to create cross-sectional images (slices) of the internal organs.
Advantages
More thorough and detailed than an X-ray image, CT can identify complex traumas or anomalies, properly localizing them in 3D.
Сautions
The carcinogenic effect is similar to X-rays, not recommended for routine scannings, people with tumor risks, or pregnant patients.
Magnetic resonance imaging
Approach
MRI employs strong magnetic fields, their gradients, and radio waves to generate images of a body’s soft- and hard-tissue organs.
Advantages
MRI provides better contrast in images of soft tissues than X-ray or CT scans. It does not pose a cancer risk and can be used for treatment.
Сautions
Less comfortable for patients, MRI doesn't fit people with metal pieces in the body. Also, MRI with contrast agents isn’t recommended during pregnancy.
Ultrasound
Approach
Sends pulses of ultrasound into body tissues, then catches their echo with different reflection properties to form an image of the particular organ.
Advantages
Safer than other imaging techniques, portable and shows organs’ functions in real time. Recommended during pregnancy.
Сautions
The image quality of ultrasound scanners might be poorer than that of other imaging medical devices. Not suitable for harder tissues.
Nuclear imaging, including positron-emission tomography
Approach
Catches the radiation signal from radionuclides absorbed by body tissues to form 2D and 3D images of the organs.
Advantages
Shows how the organs function rather than their appearance and localization. Detects anomalies earlier than CT and MRI.
Сautions
Risk of allergy to the radioactive agent, insignificant risk of cancer due to radiation. Not recommended for children or during pregnancy.
Medical image file formats
Our solutions are compatible with all of the popular medical image file formats.
DICOM
MRC
ECAT7
Interfile
Analyze
NIfTI
RAW
File formats
Start drawing more insights from medical images today
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.
Challenge
Possible solution
Challenge
Small-scale medical datasets
Challenge
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.
Challenge
Lack of annotated and balanced data
Challenge
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.
Challenge
The "black box" effect: lack of algorithm explanation
Challenge
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.
Challenge
Pushback from some healthcare professionals
Challenge
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.
1
Preparation
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.
2
Training
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.
3
Tuning
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.
Related services
Healthcare data analytics
We combine multiple analytic methods and tech to look into medical data of various formats and origins for diagnostics, prediction, and prevention.
Computer vision
Itransition trains and deploys custom ML models to enable advanced image recognition, visual search, and robotic vision, integrating these capabilities into web, mobile, and embedded platforms.
Machine learning
Our team participates in ML projects for different industries, including retail, finance, and healthcare. We’ll consult you on the optimal solution and create software that meets your key business needs.
Healthcare software development
We provide a full range of services, including consulting, design, development, maintenance, and upgrade of medical apps that enable healthcare interoperability, better patient engagement, more accurate diagnostics, and improved health outcomes.
Medical image analysis FAQ
What is DICOM, and why is it used for medical imaging?
DICOM is an international standard for medical images and related information and is used to ensure intercompatibility and security of medical image data.
Is image analysis the same as image processing?
Medical image processing is one of the techniques used for medical image analysis. Therefore, while they are often used interchangeably in some contexts, they are essentially different.
What medical devices are used for medical imaging?
A CT scanner, MRI tube, X-ray and mammography machine, ultrasound machine, echocardiography device, and many more are used to produce digital medical images.
Why is machine learning used in medical image analysis?
Modern medical imaging tools deliver a high amount of data that is hard to process, even for the most seasoned professional. ML algorithms, on the other hand, are sophisticated enough to distinguish even the most complex patterns, which makes them a perfect technology for analyzing images.
Does medical image analysis software have limitations?
Medical image analytic algorithms are just as thorough and accurate as the developers made them to be. Thus it is vital to collaborate with a trustworthy and experienced vendor.
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