Introduction

Why image segmentation?

In machine learning, image segmentation is a computational process for obtaining a set of pixel-wise labels that identify the objects in an image. This process is used in many applications, such as medical imaging, autonomous driving, and image editing. The goal of image segmentation is to divide an image into regions that correspond to different objects or parts of objects.

What does multiple annotators mean?

In the context of image segmentation, multiple annotators refer to the situation where more than one person is responsible for labeling the images. This is a common situation in medical imaging, where the same image can be labeled by different experts. The goal of Segmentation TCGE is to combine the labels of multiple annotators to obtain a more accurate segmentation of the images.