Emily Clarke

What is a Polygon Annotation Type?

In healthcare AI and machine learning, annotation helps systems make sense of the information they view. It allows machines to learn, improving efficiency and accuracy. Annotation is one piece of the puzzle, but it is critical in helping providers obtain more precise and relevant information to help patients. Immerse yourself in the world of images with the NIFTI image viewer - experience it now! https://encord.com/dicom/

There are many different types of annotation. Labelers can use a NIFTI image viewer for segmentation, bounding boxes, classification, etc. However, polygon annotation is one of the most precise forms of annotation for medical imaging.

Understanding Polygon Annotation

This type of annotation allows labelers to select X and Y coordinates to define an object's edges. It's common in object detection systems because it's versatile enough to work for irregular shapes. For example, polygon annotation can help systems detect specific organs, cysts and irregularities. It may also localize objects in the image, giving health care providers a more comprehensive look at what's happening in a patient's body.

There are a couple of reasons why polygon annotation is so effective.

First, it's pixel-perfect. In AI and machine learning, precision is key to successful training and deployment. With polygon annotation, you use coordinates on the X and Y axis to create a pixel-perfect edge boundary. That means irrelevant pixels aren't part of the annotation. Other annotation types may include stray pixels that impact the system's accuracy.

Another benefit of polygon annotation is its ability to label irregular shapes. Medical imaging is complex. Looking at brain imaging data on a NIFTI image viewer unveils distinct and irregular shapes that can vary from one patient to the next. Predefined boundary boxes are less efficient in these cases. But polygon annotation ensures that labelers can properly annotate every target object.

Are There Any Disadvantages?

Polygon annotation is perfect for complex irregular shapes. Its precision can also make a substantial difference when training AI systems. But there are some drawbacks.

Manual polygon annotation can take significantly longer than bounding boxes, especially when working with complex objects. Furthermore, some annotation tools may not be able to make holes within the polygons or assign a relationship between two polygons belonging to the same target.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at AI labeling blog.