What is a mask in image segmentation?

A mask in image segmentation is a matrix or array that identifies specific regions of interest in an image. It acts like a stencil, marking which pixels belong to a particular object, class, or area. Each pixel in the mask corresponds to a pixel in the original image, and its value determines whether that pixel is part of the segmented region. For example, in binary segmentation, a mask might use 0s for background pixels and 1s for the foreground (e.g., a person in a photo). More complex masks can use integers to represent multiple classes, like 1 for roads, 2 for cars, and 3 for pedestrians in an autonomous driving dataset.

Masks are typically created algorithmically or through manual annotation. In practice, developers use them to isolate objects for tasks like object detection, medical imaging analysis, or background removal. For instance, a mask could highlight tumors in an MRI scan by marking affected pixels, allowing doctors to analyze them separately. Tools like OpenCV, TensorFlow, or PyTorch provide functions to apply masks to images. A common approach involves using a neural network (like U-Net) to generate a mask by predicting pixel-wise class probabilities. The mask is then thresholded (e.g., converting probabilities >0.5 to 1s) to produce a binary output. Masks can also be stored as grayscale images, where intensity values map to classes, or as RGB images with color-coded regions.

The practical value of masks lies in their ability to enable precise pixel-level control. For example, in satellite imagery, a mask can differentiate between forests, urban areas, and water bodies. In video editing, masks help blur backgrounds or replace skies. Developers often use metrics like Intersection over Union (IoU) to evaluate mask accuracy against ground-truth annotations. Masks also streamline data preprocessing—such as cropping or augmenting only the masked regions—and reduce computational costs by ignoring irrelevant pixels. By isolating specific features, masks make it easier to train models, analyze results, and deploy applications like real-time object tracking or medical diagnosis systems.