Artificial intelligence (AI) products are already transforming medicineimproving diagnosis, personalizing treatment and optimizing patient care. But have you ever wondered how these AI models manage to perform so well in healthcare? In fact, medical annotation plays an essential role in their development. By precisely tagging clinical data, including medical images and videos, it optimizes supervised learning of algorithms, enabling AI models to deliver accurate and reliable results (or predictions).
But how do we obtain medical data, and in what format? Among the most widely used formats for medical imaging, DICOM(Digital Imaging and Communications in Medicine) has established itself as an essential standard. It is a standard that has the merit of offering high-quality, sometimes voluminous data. This data needs to be reviewed by professionals to extract useful information. And to guarantee effective annotation that meets the requirements of AI systems, it's important to select the right annotation tools!
💡 In this article, we take a look at a selection of the best medical annotation platforms for your healthcare AI use cases. We also share with you the main criteria that will enable you to choose the tool best suited to your use case. Follow the guide!
What is medical annotation?
Definition and importance of medical annotation
Medical annotation is the process of adding precise, structured information to medical data, such as images, scans and medical records, to make them intelligible to artificial intelligence (AI) algorithms. With this medical data and metadata, it is possible to train machine learning models to recognize specific patterns and characteristics in medical data, which can improve diagnosis, treatment and research in the healthcare field.
What is the DICOM format and why is it essential for medical annotation?
The DICOM(Digital Imaging and Communications in Medicine) format is an international standard used for the storage, exchange and transmission of medical images. It is designed to ensure interoperability between medical imaging equipment, such as scanners, MRIs or ultrasound scanners, and hospital or clinic IT systems.
DICOM contains not only the images themselves, but also important metadata such as patient information, acquisition parameters and any annotations.
In medical annotation, DICOM is essential as it enables work to be carried out directly on standardized images, guaranteeing consistent annotations across different systems and equipment. This is particularly important for training artificial intelligence models, which require accurate, uniform data.
The use of DICOM also makes it possible to preserve critical information linked to images, such as the clinical context, which can be important for the correct interpretation of data. Thanks to this standard, annotations can be enriched with relevant metadata, ensuring greater reliability in the results of AI models.
How does DICOM influence annotation in medical imaging?
The DICOM(Digital Imaging and Communications in Medicine) format has a profound influence on the annotation process in medical imaging, mainly thanks to its ability to structure and standardize images and associated metadata. Here's how it impacts this field:
Image standardization
DICOM enables uniform medical image formats from different machines and manufacturers, facilitating the use of Computer Vision for annotating medical data. This ensures that images annotated in different clinical contexts can be interpreted and used consistently by artificial intelligence systems, whatever the device or location of acquisition.
Metadata integration
DICOM contains not only images, but also critical information about the patient, the examination, and the acquisition conditions. This metadata is essential for contextualizing annotations, enabling AI algorithms to integrate important clinical elements during training and inference.
Accurate, faithful annotations
The DICOM format enables annotations to be stored directly in the image files, without altering the original data. This guarantees maximum precision in locating annotated areas, an essential element for AI models that depend on data quality to generate reliable predictions. This feature is particularly relevant in the field of Data Labeling, where it is more usual to store images and metadata separately (in JSON or XML files, for example).
Interoperability with other systems
DICOM is designed to integrate with systems such as PACS(Picture Archiving and Communication Systems) and AI platforms, and can be used in any language thanks to its multilingual support. This compatibility facilitates the transfer of images and annotations from one platform to another, guaranteeing continuity in data processing.
Volumetric annotation support
When annotating three-dimensional imaging, such as CT or MRI scans, DICOM supports the manipulation of image volumes. This enables annotators to mark structures across multiple image slices, offering richer, more complete annotation for AI model training.
Medical annotation applications in medical imaging
Using medical imaging to diagnose and monitor disease
Medical annotation is essential for medical imaging, providing AI algorithms with precise, structured information for disease diagnosis and monitoring. Medical annotations can include information such as regions of interest, lesion characteristics and medical examination results. This information can be used to train machine learning models to recognize specific patterns and features in medical images, which can improve disease diagnosis and treatment.
Automate the most laborious tasks to save time and resources
Medical annotation saves time and resources by automating tasks such as disease detection and medical image segmentation. Thanks to the use of artificial intelligence, healthcare professionals can concentrate on more complex and critical tasks, letting algorithms handle voluminous and repetitive data. For example, automatic annotation of DICOM images can speed up the diagnostic process, enabling faster patient management. What's more, automation reduces human error, increasing the reliability of results. In short, using AI does not replace a human diagnosis, but enhances the professional's ability to make his or her diagnosis!
What are the best DICOM-compatible open-source medical annotation tools?
Open Source medical annotation tools compatible with the DICOM format are increasingly used due to their flexibility, adaptability and low cost. Here are some of the best tools currently available:
ITK-SNAP
ITK-SNAP is an Open Source software package widely used for medical image segmentation, particularly in 3D. It lets you visualize and annotate images in DICOM format, while offering advanced features such as semi-automatic segmentation based on active contours. ITK-SNAP is particularly appreciated for its ease of use and ability to handle large volumes of data. Recent scientific papers have used ITK-SNAP for segmentation tasks, demonstrating its effectiveness and credibility in the field of medical imaging.
3D Slicer
3D Slicer is a popular medical visualization and annotation tool, compatible with the DICOM format. It is ideal for annotating volumetric data (such as CT and MRI scans) and offers a wide range of modules for image analysis and image segmentation. Its modular architecture enables users to customize workflows to their specific needs.
MedSeg
MedSeg is another Open Source tool dedicated to medical image annotation. Although simple to use, it integrates powerful features for DICOM image annotation, and is compatible with various types of medical images. Its light weight and user-friendly interface make it a good option for those looking for a fast, efficient solution.
CaPTk (Cancer Imaging Phenomics Toolkit)
CaPTk is an Open Source software package specialized in medical image analysis, particularly for cancer research. It supports the DICOM format and offers tools for annotation, segmentation and analysis of patient images. Although geared more towards oncology research, it offers flexibility for other types of medical image analysis.
NiftyNet
NiftyNet is an open-source framework dedicated to the annotation and analysis of medical images. Although primarily used for segmentation and deep learning, it supports the DICOM format and can be used to annotate large-scale medical images.
NiftyNet is often preferred by users looking to integrate annotation with Deep Learning algorithms to develop advanced AI models.
🪄 All these tools are particularly well suited to research projects or training AI models requiring precise, personalized annotations. What remains is to demonstrate their performance at scale, particularly when it comes to processing large volumes of medical videos... what about paid solutions?
And what about paid medical annotation applications?
Tools such as V7, Encord and Supervisely stand out for their advanced functionality and ability to process massive volumes of complex medical data, such as MRIs, X-rays and histopathological images. These platforms enable the creation of accurate, high-quality annotations, essential for training machine learning models for critical tasks.
These platforms are designed to be scalable, and can handle large volumes of data at scale (including video). Find out below how these platforms can help you set up efficient medical data annotation workflows .
V7
V7 is one of the most popular platforms for medical image annotation. Thanks to its data management capabilities and its integration of deep learning algorithms, V7 simplifies the annotation of complex images. The platform offers tools for segmentation, object object detection and 3D annotation tools, making it the preferred choice for projects requiring high precision. What's more, V7 includes an automation function that reduces annotation time while guaranteeing consistent, high-quality results. What's more, of all the tools we've tested, V7 is certainly THE platform that enables very precise tracking of annotation tasks - thanks to simple, effective dashboards for monitoring the activities of the Data Labelers.
Encord
Encord offers a complete solution for the management and annotation of medical data. Prized by many healthcare and AI specialists, this platform offers team collaboration tools, enabling multiple annotators to work simultaneously on shared datasets. Encord also stands out for its AI-assisted annotation features, which speed up the labeling process while ensuring greater reliability of results. It is particularly well suited to complex projects requiring rigorous traceability and regulatory compliance.
Supervisely
Finally, Supervisely is another key platform for medical annotation. Renowned for its flexibility and powerful analysis capabilities, Supervisely offers annotation tools adapted to various types of medical data, including 2D and 3D images. What sets Supervisely apart is its customization capabilities, which enable users to create workflows tailored to the specific needs of each project.
What are the main criteria for choosing a medical annotation tool?
The choice of a medical annotation tool is based on several key criteria to ensure quality, efficiency and suitability for the specific needs of the healthcare and AI fields. Here are the main elements to consider:
Compatible with DICOM format
As DICOM is the standard for medical imaging, it's essential to choose a tool that can read, manipulate and annotate DICOM files directly. This enables you to work seamlessly with essential images and metadata.
Precision and ease of use
The tool must offer precise functionality to accurately annotate areas of interest on medical images. An intuitive, ergonomic user interface is also essential to enable annotators, whether medical experts or not, to perform complex annotations with ease.
Automation features
Some tools incorporate AI-based features or machine learning algorithms to speed up the annotation process by predicting contours or areas of interest. This can significantly reduce the time needed to annotate large quantities of data.
Collaborative project management
For large-scale projects, it is important that the tool enables collaboration between several annotators and experts. Functions such as task tracking, annotation review and role management are key to ensuring annotation quality.
Interoperability and integrations
The tool must be able to integrate with other systems or software used in medical workflows, such as PACS(Picture Archiving and Communication Systems) or AI model training platforms. Good interoperability facilitates the export of annotated data for use in other contexts.
Data security and confidentiality
Given the sensitive nature of medical data, the tool must comply with security and confidentiality standards, such as RGPD or HIPAA, to guarantee the protection of patient information.
Scalability and management of large volumes of data
For large-scale annotation projects, the tool must be able to handle vast quantities of medical images, while ensuring processing speed and stability.
What are the advantages of DICOM-based annotation tools for training AI models?
Annotation tools based on the DICOM format offer several advantages for training artificial intelligence models in the medical field. Here are the main benefits of these tools:
1. Data standardization
DICOM is a universal standard for storing and managing medical images. Annotation tools based on this format ensure that data is uniform and compatible with a wide range of medical systems. This standardization facilitates the creation of datasets datasets, essential for training AI models.
2. Inclusion of clinical metadata
DICOM integrates not only images, but also detailed metadata on the patient, the imaging device, and the clinical context. This additional information can enrich the learning of AI models by providing important medical context, improving the accuracy of predictions and diagnoses.
3. Precise segmentation and annotation
DICOM-based annotation tools enable the manipulation of 2D and 3D medical images, offering high precision in the segmentation of tissues, organs or abnormalities. This is essential for training AI models in complex tasks, such as tumor detection or disease classification.
4. Support for volumetric data
Medical images such as MRI or CT scans are often volumetric, i.e. 3D, and DICOM-based tools can handle these types of data. This enables annotations to be made across different image slices and provides richer information to AI models, improving their ability to recognize complex patterns .
5. Interoperability
DICOM-based tools integrate easily with other hospital systems and AI model training platforms. This makes it easy to transfer annotated images between different systems, while ensuring that annotations and images remain compatible and usable by different analysis or machine learning software.
6. Privacy and security management
Given the sensitivity of medical data, DICOM-based tools are often compliant with strict data protection regulations such as RGPD and HIPAA. They enable annotations to be managed while ensuring that patients' personal information is protected, an essential aspect for AI model training projects in the medical field.
7. Reduced annotation time and costs
Some DICOM-based annotation tools integrate automation functions, such as machine learning for the prediction of contours or regions of interest. This speeds up the annotation process, reducing the time and costs involved in preparing datasets for AI.
How does the choice of a medical image annotation tool impact the performance of AI models?
The choice of a medical image annotation tool has a direct and significant impact on the performance of AI models. Here are the main ways in which it influences the results of AI models:
Annotation quality
Accurate annotation is essential for training AI models. A high-performance tool must enable annotators to accurately mark areas of interest on medical images, such as tumors or abnormalities. High-quality annotations provide models with reliable data, improving their ability to learn and generate accurate predictions.
Consistency and standardization
An annotation tool that ensures the standardization of annotations, notably via the DICOM format, enables data to be standardized across different projects and systems. This reduces variation errors between annotations, ensuring consistency in training AI models, resulting in more robust and generalizable performance.
Volumetric data management
Medical images, such as MRI or CT scans, are often in 3D. A tool capable of handling these image volumes enables more complete annotation, taking into account the different dimensions. This provides AI models with richer, more accurate information, improving their ability to interpret complex structures in medical data.
Automation and efficiency
Some annotation tools incorporate automation features, such as automatic prediction of contours or regions of interest, based on machine learning algorithms.
These features reduce annotation time while guaranteeing sufficient accuracy, speeding up the data preparation process and enabling faster training of AI models without compromising quality.
Integration with machine learning systems
Choosing a tool that is compatible with machine learning and AI model training pipelines, as well as with standard data formats such as DICOM, facilitates the transition from annotated data to AI systems.
This ensures that models receive annotated data without loss of information or integrity, maximizing their ability to learn from this data.
Conclusion
Choosing the right medical annotation tool is crucial to the effective training of artificial intelligence models in the healthcare field. Thanks to the standardization and advanced capabilities of the DICOM format, these tools can handle complex volumetric data while guaranteeing the accuracy essential for clinical applications.
Whether it's ensuring compatibility with hospital systems, integrating relevant metadata or complying with patient confidentiality standards, DICOM-based annotation tools play a central role in the development of better, more reliable AI solutions.
By selecting a tool that meets the specific needs of your project, you optimize not only the effectiveness of annotations, but also the results of the AI models that depend on them. Do you have a medical AI project you'd like to discuss with us? Are you looking for medical specialists and experts capable of annotating your most complex and/or voluminous data? Don't hesitate to contact us.
