How to choose the best medical annotation tools for AI: Focus on the DICOM format
Artificial intelligence (AI) products are already transforming medicine, improving diagnoses, personalizing treatments, and optimizing patient care. But have you ever wondered how these AI models manage to be so effective in healthcare? In fact, medical annotation plays a critical role in their development. By accurately tagging clinical data, including medical images and videos, it optimizes the supervised learning of algorithms, allowing AI models to provide accurate and reliable results (or predictions).
But how do you get medical data, and in what format? Among the most 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 quality data, which is sometimes voluminous. This data must be reviewed by professionals to extract useful information. And in addition, to ensure effective annotation that meets the requirements of AI systems, it is important to select the appropriate annotation tools!
💡 In this article, we suggest that you discover a selection of the best medical annotation platforms for your AI health use cases. We also share with you the main criteria that will allow you to choose the most suitable tool for your use case. Follow the guide!
What is a medical annotation?
Definition and importance of medical annotation
Medical annotation is the process of adding accurate, structured information to medical data, such as images, scans, and medical records, to make it 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 health research.
What is the DICOM format and why is it essential in medical annotation?
The DICOM format (Digital Imaging and Communications in Medicine) is an international standard used for the storage, exchange and transmission of medical images. It was designed to ensure interoperability between medical imaging equipment, such as scanners, MRIs, or ultrasound scanners, and hospital or clinic computer 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 because it allows you to work directly on standardized images, guaranteeing the consistency of annotations across different systems and equipment. This is especially important for training artificial intelligence models, which require accurate and consistent data.
The use of DICOM also makes it possible to maintain critical image-related information, such as the clinical context, which can be important for the correct interpretation of the data. Thanks to this standard, annotations can be enriched with relevant metadata, ensuring better reliability in the results of AI models.
How does DICOM influence annotation in medical imaging?
The DICOM format (Digital Imaging and Communications in Medicine) profoundly influences the annotation process in medical imaging, mainly thanks to its ability to structure and standardize images and associated metadata. Here is how it impacts this area:
Standardization of images
DICOM allows for uniformity in medical image formats from different machines and manufacturers, thus facilitating the use of Computer Vision for the annotation of medical data. This ensures that annotated images in different clinical contexts can be interpreted and used consistently by artificial intelligence systems, regardless of the device or location of acquisition.
Metadata integration
DICOM contains not only images, but also critical information about the patient, exam, and acquisition conditions. This metadata is essential for contextualizing annotations, allowing AI algorithms to incorporate important clinical elements during training and inference.
Precision and fidelity of annotations
The DICOM format allows annotations to be stored directly in image files, without altering the original data. This ensures maximum accuracy in locating annotated areas, a critical component for AI models that rely on data quality to generate reliable predictions. This characteristic is particularly relevant in the field of Data Labeling, where it is more common to store images and metadata separately (for example in JSON or XML files).
Interoperability with other systems
DICOM is designed to integrate with systems like 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.
Support for volumetric annotations
For the annotation of three-dimensional imaging, such as CT or MRI scans, DICOM supports the manipulation of image volumes. This allows annotators to mark structures across multiple image slices, offering richer and more comprehensive annotation for training AI models.
Applications of medical annotation in medical imaging
Use of medical imaging for the diagnosis and monitoring of diseases
Medical annotation is essential for medical imaging, as it provides accurate and structured information to AI algorithms for the diagnosis and monitoring of diseases. Medical annotations may include information such as regions of interest, injury characteristics, and medical exam results. This information can be used to train machine learning models to recognize specific patterns and characteristics in medical images, which can improve the diagnosis and treatment of diseases.
Automating the most laborious tasks to save time and resources
Medical annotation saves time and resources by automating tasks, such as disease detection or medical image segmentation. Through the use of artificial intelligence, healthcare professionals can focus on more complex and critical tasks, leaving algorithms to deal with big, repetitive data. For example, automatic annotation of DICOM images can speed up the diagnostic process, allowing for faster patient care. In addition, automation reduces human errors, thus increasing the reliability of results. In short, using AI does not replace a human diagnosis, but improves the professional's ability to make a diagnosis!
What are the best open source medical annotation tools compatible with DICOM?
Open source medical annotation tools compatible with the DICOM format are increasingly being used due to their flexibility, adaptability, and reduced cost. Here are some of the best tools out there right now:
ITK-SNAP
ITK-SNAP is an Open Source software widely used for the segmentation of medical images, especially in 3D. It allows you to view 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 its ability to manage big data. Recent scientific articles have used ITK-SNAP for segmentation tasks, thus demonstrating its effectiveness and credibility in the field of medical imaging.
3D Slicer
3D Slicer is a very popular medical visualization and annotation tool that is compatible with the DICOM format. It is ideal for annotating volumetric data (such as CT and MRI scanners) and offers a wide range of modules for analysis and image segmentation. Its modular architecture allows users to customize their workflows according to their specific needs.
MedSeg
MedSeg is another Open Source tool dedicated to the annotation of medical images. Although it is simple to use, it includes powerful features for annotating DICOM images and is compatible with various types of medical images. Its lightness and user-friendly interface make it a good option for those looking for a quick and effective solution.
CAPTK (Cancer Imaging Phenomics Toolkit)
CapTK is an Open Source software specialized in the analysis of medical images, especially for cancer research. It supports the DICOM format and offers tools for the annotation, segmentation, and analysis of patient images. Although it is more oriented 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 it is mostly used for segmentation and deep learning, it supports the DICOM format and can be used to Annotate images medical services on a large scale.
NiftyNet is often preferred by users looking to integrate annotation with deep learning algorithms to develop advanced AI models.
All these tools are particularly suitable for research or training projects for AI models requiring accurate and personalized annotations. It remains to demonstrate their performance at scale, especially when it comes to processing large volumes of medical videos... what about paid solutions?
And on the side of paid medical annotation applications?
Tools like V7, Encord, and Supervisely stand out for their advanced functionalities and their ability to process massive volumes of complex medical data, such as MRIs, X-rays, or histopathological images. These platforms make it possible to create accurate and high-quality annotations that are essential for training machine learning models for critical tasks.
These platforms are designed to be scalable, and allow large volumes of data to be processed at scale (especially videos). Find out below how these platforms can help you set up workflows efficient annotation of medical data.
V7
V7 is one of the most popular platforms for annotating medical images. With its data management capabilities and integration of deep learning algorithms, V7 simplifies the annotation of complex images. The platform offers segmentation tools, object detection and 3D annotations, making it a preferred choice for projects requiring high precision. Additionally, V7 includes an automation feature that reduces annotation time while ensuring consistent, high-quality results. Moreover, of all the tools we tested, V7 is certainly THE platform that allows very precise monitoring of annotation tasks - thanks to dashboards simple and effective to ensure the monitoring of the activities of Data Labelers.
Encord
Encord offers a complete solution for the management and annotation of medical data. Popular with many health and AI specialists, this platform offers tools for collaboration between teams, allowing several annotators to work simultaneously on shared data sets. Encord also stands out for its AI-assisted annotation features, which speed up the labeling process while ensuring more reliable results. It is particularly suited to complex projects requiring rigorous traceability and regulatory compliance.
Supervisely
Finally, Supervisely is another essential platform in the field of medical annotation. Recognized for its flexibility and powerful analysis capabilities, Supervisely offers annotation tools adapted to various types of medical data, including 2D and 3D images. Its particularity lies in its customization features, which allow users to create workflows adapted 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 in order to guarantee quality, effectiveness and adequacy with the specific needs of the health and AI field. Here are the main things to consider:
DICOM compatibility
Since DICOM is the standard for medical imaging, it is essential to choose a tool that can read, manipulate, and annotate DICOM files directly. This makes it possible to work fluidly with essential images and metadata.
Precision and ease of use
The tool must offer specific functionalities to accurately annotate areas of interest on medical images. An intuitive and ergonomic user interface is also essential to allow 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 dramatically reduce the time needed to annotate large amounts of data.
Management of collaborative projects
In the context of large-scale projects, it is important that the tool allows collaboration between several annotators and experts. Features such as task tracking, annotation review, and role management are key elements in ensuring the quality of annotations.
Interoperability and integrations
The tool should be able to integrate with other systems or software used in medical workflows, such as PACS (Picture Archiving and Communication Systems) or platforms for training AI models. Good interoperability makes it easy to export annotated data for use in other contexts.
Data security and confidentiality
Given the sensitive nature of medical data, the tool must meet security and privacy standards, such as GDPR or HIPAA, to ensure patient information is protected.
Scalability and big data management
For large-scale annotation projects, the tool must be able to manage vast quantities of medical images while ensuring processing speed and stability.
What are the benefits 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. Standardization of data
DICOM is a universal standard for storing and managing medical images. Annotation tools that rely on this format ensure that data is consistent and compatible with a wide range of medical systems. This standardization makes it easy to create datasets that are consistent, essential for training AI models.
2. Inclusion of clinical metadata
DICOM integrates not only images, but also detailed metadata about the patient, the imaging device, and the clinical context. This additional information can enrich the learning of AI models by providing important medical context, which in turn improves the accuracy of predictions and diagnoses.
3. Precision in segmentation and annotation
DICOM-based annotation tools make it possible to manipulate medical images in 2D and 3D, offering great precision in the segmentation of tissues, organs or anomalies. This is essential for training AI models in complex tasks, such as detecting tumors or classifying diseases.
4. Support for volumetric data
Medical images such as MRI or CT scans are often volumetric, i.e. in 3D, and DICOM-based tools can handle these types of data. This makes it possible to perform annotations across different image sections and to provide richer information to AI models, improving their ability to recognize Patterns complexes.
5. Interoperability
DICOM-based tools easily integrate with other hospital systems and AI model training platforms. This allows annotated images to be easily transferred between different systems, while ensuring that annotations and images remain compatible and usable by different analytics or machine learning software.
6. Privacy and Security Management
Given the sensitivity of medical data, DICOM-based tools often comply with strict data protection regulations, such as GDPR and HIPAA. They make it possible to manage annotations 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 incorporate automation features, such as machine learning for the prediction of contours or regions of interest. This speeds up the annotation process, reducing the time and costs needed to prepare 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 artificial intelligence models. Here are the main ways it influences the results of AI models:
Quality of the annotations
Accurate annotations are critical for training AI models. A powerful tool should allow 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, in particular via the DICOM format, makes it possible to standardize data 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 scanners, are often in 3D. A tool capable of managing these volumes of images makes it possible to annotate in a more complete way, taking into account the various dimensions. This provides AI models with richer and more accurate information, improving their ability to interpret complex structures in medical data.
Automation and efficiency
Some annotation tools incorporate automation features, such as the automatic prediction of contours or regions of interest, based on machine learning algorithms.
These features reduce annotation time while ensuring sufficient accuracy, which speeds up the data preparation process and allows AI models to be trained more quickly 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 standard data formats like DICOM, makes it easier to transition annotated data into AI systems.
This ensures that models receive annotated data without loss of information or integrity, maximizing their ability to learn from that data.
Conclusion
Choosing the right medical annotation tool is crucial for the effective training of artificial intelligence models in the field of health. Thanks to the standardization and advanced capabilities of the DICOM format, these tools make it possible to process complex volumetric data while guaranteeing essential precision for clinical applications.
Whether it's ensuring compatibility with hospital systems, integrating relevant metadata, or complying with patient privacy standards, DICOM-based annotation tools play a central role in developing better and more reliable AI solutions.
By selecting a tool that meets the specific needs of your project, you not only optimize the effectiveness of annotations, but also the results of the AI models that depend on them. Do you have a medical AI project that you want to talk to us about? Are you looking for medical specialists and experts who can annotate your most complex and/or voluminous data? Do not hesitate to contact us.
