AI-driven segmentation fundamentally relies on deep learning models, most commonly Convolutional Neural Networks (CNNs) or specialized architectures like U-Net. These models undergo an intensive training phase, where they learn from vast datasets containing input data, such as images, paired with corresponding ground-truth segmentation masks. During inference, the trained network processes new, unseen data by first extracting hierarchical features through its multiple layers. Subsequently, it utilizes these learned features to predict pixel-wise labels or probabilities for every element in the input. This sophisticated process effectively delineates distinct objects or regions of interest, ultimately producing a precise segmentation map. More details: https://www.tumblr.com/blog/oksanainforblog