Segmentation Annotation
This methodology aids Artificial Intelligence models in understanding the meanings of images.
Segmentation annotation addresses the overlap problem in object detection by ensuring that each component of an image belongs to only one class. Typically performed at the pixel level, this method requires annotators to assign categories such as pedestrian, car, or even human organs.
This helps teaching an AI model to recognize and classify specific objects, even if they are obstructed.
Objects, Instances e Segments
2D (bounding boxes), 3D (cuboids), or polygon annotation techniques label individual objects in an image. However, to annotate each pixel that defines an object in an image, the semantic segmentation method is necessary.
Segmentation labeling assists computer vision in locating objects with greater precision, enabling the identification of multiple objects of the same class as a single entity
Unlike the previously mentioned techniques, in semantic annotation, labelers divide the image into a list of segmented labels. For example, in an urban scene, annotators would segment the image into vehicles, bicycles, pedestrians, obstacles, signs, buildings, streets, etc., assigning a color to each segment.
The Segmentation Annotation Types
01. Semantic Segmentation
Semantic segmentation is the process of dividing the image into multiple segments or sets of labeled pixels. It involves identifying all the pixels that compose a given object and applying a fill or mask to them.
02. Instance Segmentation
Instance segmentation is employed to train the machine learning model when there are multiple objects of the same class in the same image, but they are distinct instances.
03. Panoptic Segmentation
Panoptic segmentation is a type of image segmentation that combines instance and semantic segmentation, producing a unified result of these annotations.
Segmentation Labeling in Medicine
In the realm of Artificial Intelligence, there is a widespread consensus that successful training of AI models necessitates a large volume of annotated images.
Medical image segmentation identifies organs or lesions at the pixel level in CT scans, MRIs, x-rays, among other medical imaging modalities. This provides professionals with crucial information about the shapes and volumes of organs and potential pathologies.
This type of annotation is one of the most challenging and necessitates the active participation of a team of medical experts in the annotation process.
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