Single Slice Segmentation

Single-slice segmentation focuses on analyzing and interpreting information from a single cross-sectional image, a technique valuable for reducing data processing and storage needs, especially in applications like medical imaging and remote sensing. Current research emphasizes improving the accuracy of 2D segmentation models by leveraging information from 3D data through techniques like knowledge distillation and attention mechanisms, or by employing voting strategies to integrate multiple predictions from different parts of the image. These advancements aim to enhance the efficiency and accuracy of single-slice analysis across diverse fields, leading to improved diagnostic capabilities in medicine and more efficient processing of large datasets in other domains.