Medical Image Annotation
Medical imaging techniques, including X-rays, MRIs, and CT scans, play a vital role in the diagnosis and treatment of a wide range of medical conditions. AI models that are trained on labeled images can support radiologists and healthcare professionals in detecting abnormalities, monitoring the progression of diseases, and even forecasting patient outcomes
Bounding Box Annotation
This method involves drawing rectangles around particular areas in medical images, such as tumors or lesions, which enables AI models to identify and locate abnormalities effectively
Semantic Segmentation
This annotation technique offers pixel-wise labeling of medical images, categorizing each pixel according to the structure it represents (e.g., bone, tissue, tumor). This allows the AI to develop a comprehensive understanding of intricate medical images.
Polygon Annotation
For objects with irregular shapes, such as tumors, polygon annotation provides a precise outline of the object's contours, facilitating accurate detection and classification
Key Point Annotation
This technique entails marking specific points on a medical image, such as anatomical landmarks or organ boundaries, assisting the AI model in tracking structures or measuring changes over time