Prior Segmentation

Prior segmentation, the process of pre-segmenting an image or volume before applying a primary segmentation or other image processing task, aims to improve efficiency and accuracy of downstream applications. Current research focuses on developing robust and efficient pre-segmentation methods, employing techniques like harmonic Beltrami signatures for shape-guided segmentation, superpixel-based approaches for hierarchical merging, and learned image transformations informed by class-agnostic segmentation masks. These advancements are impacting diverse fields, including medical image analysis (faster annotation, improved stain translation), image compression (bitrate reduction), and computer vision (enhanced object detection and metal artifact reduction in medical imaging). The ultimate goal is to leverage prior segmentation to improve the accuracy and efficiency of various image processing and analysis tasks.