Healthcare AI is one of the most promising avenues in the healthcare sector and relies heavily on machine learning algorithms to analyze medical data to make predictions or assist in diagnosis and treatment decisions. It implies that data annotation is crucial in healthcare AI development and implementation. Diverse sources such as medical imaging devices, diagnosis documents, visual observations, and health data collection applications contribute to the vast influx of data into the healthcare industry. Whether visual (image-like) or textual form, this data serves various purposes, ranging from clinical and research applications to administrative functions.
Nevertheless, a common characteristic of this raw data is its lack of structure and labels. Ground truth labels for medical data are essential to address this crucial need. Hospitals, universities, and private research institutes are investing time and effort to ingest this labeled medical data and have state-of-the-art models assist the healthcare industry at scale.
In this guide, we delve into the various types and challenges of medical image annotation while highlighting the crucial factors to consider when choosing a medical annotation tool and partner. Through this blog, we will uncover the vital role that medical image annotation plays in unlocking the full potential of AI in healthcare.
What is Medical Image Annotation and Labeling?
Medical image annotation and labeling are the processes of adding metadata to medical images to make them machine-readable. However, there are some differences between the two.
- Annotation includes adding additional information to an image, such as labels, bounding boxes, or segmentation masks. This information can help train machine learning models to identify and analyze medical conditions.
- Labeling is the process of assigning a single label to an image. This label can be a simple category, such as normal or abnormal, or something more complex, such as the location and size of a tumor.
Labeled medical data is used in two main ways:
Research: Researchers use labeled medical data to train machine-learning models for developing new medical treatments and diagnostic tools. It is a critical step in building artificial intelligence (AI) for healthcare, as it allows researchers to test and improve their models on large datasets of real-world data.
Clinical applications: Labeled medical data has many clinical applications, such as radiology and anomaly detection. Machine learning models can help radiologists diagnose diseases by analyzing medical images. Anomaly detection models can help identify patients at risk for certain diseases or those experiencing abnormal behavior.
Both research and clinical applications of labeled medical data are critical for AI advancement in healthcare. Researchers use labeled data to develop new AI tools, while clinical applications use AI tools to improve patient care.
Medical Data Preparation
Machine learning models are becoming increasingly important in the healthcare industry, as they can automate tasks, improve diagnostic accuracy, and develop new treatments. However, to train a machine learning model that will give reliable results, it needs training with a decent amount of data labeled at the highest quality.
The process of preparing medical data for annotation involves a few key steps:
Variety of datasets: Your data must not come from the same source or should not look the same. For improved reliability, you need the model trained with varied datasets. If your model is trained only on a subset of data or on data that all look very similar, it will not know what to do when we show data that looks different.
Dataset vetting process: Once you have a variety of datasets, you must vet them to ensure high quality. It includes checking for errors, inconsistencies, and missing data. Splitting your dataset across training, validation, and testing is also helpful. Training will comprise about 80% of your data.
Size of your dataset: Recent developments in ML have shown that quality is as important as quantity when it comes to training models. It means that a small but high-quality dataset will usually perform equally or even better than a large set of lower quality. That said, if you have the option to enlarge your dataset, we highly recommend doing so, as model results will improve significantly.
Format of your dataset: The two most common medical imaging formats include DICOM and TIFF files. DICOM, especially, is the industry standard for radiologists. DICOM and TIFF files can optionally contain multiple images or slices and metadata regarding the patient and the image itself. Good medical image annotation platforms will support both formats.
What makes medical image annotation different from general data annotation?
Annotating and labeling images for healthcare is an altogether different endeavor compared to regular image annotation. Here are some things that are different:
Data Availability
While regular images are often freely available or behind a standard NDA, medical imaging is usually protected by strict data processing agreements. It is mainly to protect the privacy of the patient. Obtaining medical imaging data is usually challenging as compared to other data types.
Technical Differences
Regular images only have one layer, are of small size, and have a low bit depth. Medical images often have multiple layers (slices), are large, and have a higher bit depth.
Further, the labeler profiles for both will be different. Generalist data annotators can work on almost all images, but medical imaging annotation requires specialized healthcare experts.
These experts are used to certain UI and UX paradigms. Therefore, when choosing a data labeling platform, it is critical to note whether medical professionals can easily use its keyboard controls and UI.
Types of Labeling Tasks
In healthcare, image classification means assigning a label to an image based on its medical contents. For example, an image classification model could be trained to label images as normal or abnormal.
Object detection is more complex than image classification in the healthcare domain. In addition to labeling an image, object detection also identifies the location of the object in the scene. For example, an object detection model could be trained to identify and locate tumors in medical images.
Image segmentation is a task that breaks an image down into smaller components in the healthcare domain. There are two main types of image segmentation- semantic and instance segmentation.
- Semantic segmentation labels each pixel in an image with a category label. For example, a semantic segmentation model could label each pixel as lung, heart, vessel, or tumor.
- Instance segmentation labels each object in an image with a unique label. For example, an instance segmentation model could give a unique label to each tumor.
Types of Medical Image Annotation
- Bounding box: A bounding box is a rectangular region that encloses an object in an image. It is the simplest and most common type of annotation.
- Polygon: A polygon is a closed figure made of multiple line segments for annotating objects that have irregular shapes.
- Keypoints: Keypoints represent the location of specific features in an image. They are often used to annotate objects that have a small size or that are difficult to identify with a bounding box or polygon.
- Landmark: A landmark denotes a specific point of interest in the image, such as the nose tip or tumor center. Landmarks are primarily used for registration tasks, which involve aligning two or more images of the same object.
- Point cloud: A point cloud is a collection of points that represent the 3D coordinates of an object. It is used for registration and volumetric segmentation tasks.
Biggest Challenges in Medical Data Annotation
Privacy
In the medical domain, the data collected is highly personal and subject to privacy regulations. When using a cloud platform for data labeling or outsourcing the labeling process, it is crucial to ensure that the data is handled with strict privacy and security regulations.
iMerit addresses this by baking the medical anonymizer service directly into the platform. It means that when data is uploaded, it goes through a layer of anonymization that removes all patient and institution-specific details before a labeler sees the data.
Expertise
Another challenge of medical data labeling is the need for domain expertise to label the data. Medical data is complex, and an untrained labeler may struggle to annotate it correctly. It is where the experience and qualifications of radiologists and radiographers come in.
iMerit ensures a rigorous recruitment process to select capable and experienced medical professionals in radiology, robotic surgery, and pathology to ensure the labels are as accurate as possible.
Formats
Medical imaging data comes in formats that are different from traditional image formats. These formats are more robust and suited to the needs of medical systems and professionals. However, this makes them more complex, making compatibility with different platforms an issue.
The iMerit labeling platform supports all types of DICOMs: series, single- and multi-frame, 8- and 12-bit, color/BW, 3D, and more.
Choosing the Right Medical Image Annotation Tool
DICOM viewers with annotation capabilities abound in the market. One notable open-source option, for example, is 3D Slicer.
DICOM viewing tools, however, are not optimized for ML model training. Sometimes it is simply impossible to use the labels from these viewers in machine learning. It is due to a lack of instance IDs and structured export formats.
You must use a professional medical imaging labeling tool to train and develop a neural network. The image annotation tool you choose will have to satisfy these requirements:
- Does the solution support medical formats such as DICOM and TIFF?
- Does it support the labeling tools you are looking for?
- Is the UX easy to use and suitable for medical use?
- Is the export format easy to use in ML model training?
- Does the solution have a medical data labeling service to enhance your workforce?
Have you explored our cutting-edge Radiology Annotation Suite? Click here to discover its powerful capabilities.
5 Key Questions to Ask Before Outsourcing Healthcare Data Labeling
- What are your privacy and security standards? Healthcare data is highly-sensitive, and it is crucial that the company you outsource it to has strong privacy and security standards in place. Ask about their data encryption methods, access control policies, and disaster recovery procedures.
- What is your experience with medical data labeling? Not all companies have experience with medical data labeling. Ask about the work with labeling different types of medical images and their experience with working with healthcare organizations.
- What is your quality assurance process? Ask about their quality assurance procedures, as well as their turnaround times for quality assurance checks.
- What is the pricing? Healthcare data labeling can be expensive, so you must get quotes from multiple companies before deciding. Ask about the pricing structure and discounts they may offer for long-term contracts.
- Will subject matter experts be involved? SMEs are healthcare professionals with specialized knowledge in a particular area, such as radiology or pathology. SMEs can be involved in the labeling process in several ways. They can train the labelers, review the labels created, or participate in the quality assurance process. Ask about their workforce, training capabilities, and response time for customer inquiries.
By asking these questions, you can ensure that you choose a company that provides high-quality, accurate, and secure medical data labeling services.
It is always good to start partnering up with a company that has already invested the time and effort required to comply with the various data formats, regulatory requirements, and user experience needed for a successful medical AI data annotation project. iMerit is one of the leading global data annotation providers and has been rated #1 in healthcare data labeling by i360 Research, September 2022 edition.
What Sets iMerit Apart?
Fully-managed Teams with Tiered Expertise to Ensure Quality
iMerit medical division has three tiers of expertise: a curriculum-driven workforce by clinicians and SMEs, specialized annotators for QC, and US board-certified doctors for benchmarking and validation. The ecosystems allow us to provide the right level of service for any project complexity.
Tech Enabled & Tool Agnostic Approach for Maximum Productivity
iMerit data experts can work on various annotation tools, including proprietary solutions and other third-party tools. Our Data Studio platform provides a single end-to-end solution for managing configuration, annotation, project progress, and analytics.
Rapid Scalability
iMerit has a team of 100 data experts who have enriched over 20 million data points for healthcare AI. Their low employee attrition rate and specialized learning & development programs create a robust skilled workforce that consistently delivers quality at scale.
Robust Security
iMerit is HIPAA certified, SOC 2 compliant, ISO 27001:2013 certified, and has been audited based on AICPA guidelines. They have over 5,500 full-time employees under strict NDA across the US, India, and Bhutan. They operate dedicated and monitored facilities with strict security protocols for high-security work.
Working with Global Companies
iMerit works with leading pharmaceutical companies, device manufacturers, health plans, and provider networks to deliver quality, secure, HIPAA-compliant data solutions, locally and off-shore. iMerit’s hybrid and custom workflows enable scalability and cost efficiency without compromising quality.
Conclusion
In this blog, we have discussed the importance of medical image annotation for developing AI-powered healthcare solutions. We have also outlined the different types of medical image annotation, the tools and techniques, and the factors to consider when choosing a data annotation partner.
