At some point, we’ve all faced medical situations where a doctor requested a CT scan, MRI, X-ray, or other internal imaging. Why? To understand our medical condition and plan appropriate treatment. However, diagnosing abnormalities can sometimes take time, and they may occasionally go unnoticed. But no more worries! The era of AI is here to assist.
The essential process of medical data annotation is at the heart of AI’s capability, particularly medical image labeling. In this blog, we’ll briefly discuss this.
Medical Image Data and Annotation
Medical image data gives visual insights into the body’s internal structures. It helps diagnose and treat health conditions. This includes X-rays, CT scans, MRIs, ultrasounds, and more, all of which play a crucial role in medical decision-making.
Medical Image Annotation is the process of preparing training data for AI models. It involves adding relevant information to images, such as labeling specific areas, identifying structures, or marking abnormalities, depending on the needs of the AI project. However, the performance of AI models directly depends on the quality of the training data provided. High-quality, error-free data ensures accurate learning and effective performance.
The Purpose
Annotated datasets are essential for AI training and healthcare applications. Labeled image data is used to teach AI models by providing a large, diverse dataset. Regardless of the model’s goal, training with sample data helps the AI system recognize patterns and perform tasks accurately. In healthcare, labeled data helps AI systems diagnose conditions by analyzing medical images, enabling more accurate predictions and timely interventions. For instance, an AI model designed for the mental health field might analyze human faces to identify facial expressions. To achieve this, the training data must include millions of labeled facial express
Once an AI tool is created, it can be implemented in the healthcare system to improve patient care. Even after successfully running, the system still needs to keep learning from new image data to stay accurate and reliable. Diseases can evolve, and medical practices may change over time. New labeled data helps keep AI tools up-to-date and ensures they remain effective.
Tasks In Image Annotation
AI models require different data labeling tasks depending on their purpose and the type of data they need to analyze. The specific data labeling tasks vary based on the model’s intended function, such as diagnosing diseases, detecting anomalies, or predicting patient outcomes. In medical image labeling, the following tasks are mainly performed:
Image Classification
Image classification involves assigning a single label to the entire medical image. For instance, classifying an X-ray as “normal” or “pneumonia” and determining whether a skin lesion is “benign” or “malignant.”
Object Detection
Structure detection involves identifying and localizing specific structures or abnormalities within a medical image. For example, detecting tumors in a brain MRI scan.
Image Segmentation
Image segmentation involves performing pixel-level classification to distinguish specific regions or structures within an image. For instance, delineating the tumor boundary in a CT scan.
Object Tracking
Object tracking involves tracking the movement of objects or abnormalities across a sequence of images or frames in a video. For instance, tracking the structures and elements in a CT scan.
Applications of Medical Image Annotation
In healthcare, AI models are assisting medical professionals in predicting, diagnosing, and treating patients and various health conditions. Annotated medical image data plays a crucial role in these applications. Wherever medical professionals rely on image data in healthcare, labeled image data can enhance those processes by enabling AI to interpret the images effectively.
To make this clearer, let’s discuss how labeled image data contributes separately to disease prediction, diagnosis, and treatment:
Early Detection and Prediction
Regardless of the disease or abnormality in our body, the best approach is early prediction and detection. If we identify the early signs of a disease, we can take timely precautions and seek treatment, leading to better health. Image annotation enables early detection, often before symptoms appear, and helps predict disease progression.
Diagnosis
Doctors use medical images, pathological slides, and other visual data to diagnose diseases. With AI, this process can be done more quickly and with fewer medical professionals. For example, Google Health’s Automated Retinal Disease Assessment (ARDA) demonstrates how AI can transform diagnosis. It uses machine learning to detect diabetic retinopathy by analyzing retinal images, with training data from over 128,175 retinal images.
Treatment
AI can support patient treatment in various ways, including personalized medicine, monitoring, and robotic surgery. For example, in robotic surgery, how does a machine know which surgical tools to use, what medicines to apply, or which patterns to operate on? All these applications rely on millions of annotated image data. This training data enables machines to understand body patterns and guide surgical tools accurately. Image annotation also helps AI models monitor patients by recognizing facial expressions and movements to assess emotions or detect potential issues.
Image Labeling techniques
Labeling techniques refer to how the medical data is annotated or labeled. Now, it’s time to explore the different medical image data labeling techniques. The choice of technique depends on the complexity of the image and the specific requirements of the AI model. Below are some common medical image labeling techniques, each with its unique approach to annotating images for tasks such as detection, segmentation, and tracking.
Bounding Box
A rectangular box is drawn around an object or region of interest in the image to define its location. The bounding box technique is used for detecting and localizing structures like tumors, organs, or lesions. for example, marking a lung tumor in a CT scan.
Polygon Annotation
A closed shape is defined by a series of connected points to create a more accurate outline of irregularly shaped objects. For instance, outlining the boundary of a tumor in an MRI scan.
Keypoint Annotation
This technique is particularly useful for tasks that require precise localization of important features, such as facial recognition, joint detection, or other anatomical landmarks. For example, in an X-ray, the joints of the hand are connected to show the structure and alignment of the bones.
Semantic Segmentation
This technique separates and classifies various parts of the image into meaningful regions for analysis, such as organs, abnormalities, or specific tissues. For example, it can be used to segment different organs, like the liver, kidneys, and lungs, in a CT scan.
Who Can Annotate Medical Image Data?
As discussed earlier, every AI model requires a large amount of training data. While some AI development teams or organizations create these datasets in-house, most prefer to approach external teams for this task. Medical images can be annotated in various ways, including automation, manual annotation, or a combination of both.
Since medical data requires high-quality datasets, most AI developers opt for manual data labeling. Automation alone can be risky as it demands expert handling, clarification of doubts, expert suggestions, and guidance. Automation tools are not yet advanced enough to handle highly sensitive and complex medical data effectively. Hence, a human touch remains essential, making manual data labeling the preferred choice.
However, maintaining an in-house team for annotating large datasets is often challenging for AI development teams. The better option is to partner with a trustworthy outsourcing company. This is where Medrays play a crucial role.
At Medrays, we have a highly trained medical data labeling team. For the past eight years, we have been serving the medical AI industry with excellence. Our facilities include the support of medical professionals, a secure environment for data handling, and access to cutting-edge tools. Our annotators are proficient in using various labeling tools such as V7, Redbrick, Dataverse, SuperAnnotate, and Ango.ai, ensuring top-quality annotation for even the most complex datasets.
Conclusion
Medical image annotation is crucial for all use cases where internal body visuals are part of diagnosis and treatment. This includes fields like ophthalmology, pathology, radiology, nephrology, and more. Every AI model that uses image analysis plays a supporting role, speeding up processes and assisting medical professionals.
In the current era, AI tools have become highly efficient, but the final decisions must always be made by human professionals. AI can predict outcomes, enabling humans to take precautions and initiate early treatment. While AI can diagnose quickly, human expertise is required to verify these diagnoses and provide comprehensive care. Additionally, AI can monitor and manage treatments efficiently, but it still requires human oversight to ensure accuracy and ethical application.
In the future, we can expect more advanced and efficient AI tools, but they will not replace human professionals. Instead, AI will serve as a powerful tool to enhance human capabilities, ensuring better outcomes in healthcare.