Deep Learning Based Segmentation

Deep learning-based image segmentation aims to automatically delineate objects or regions of interest within images, a crucial task with broad applications, particularly in medical imaging. Current research focuses on improving segmentation accuracy and efficiency through novel architectures like U-Net variants (often incorporating attention mechanisms and multi-scale features) and advanced loss functions (e.g., Dice loss variations). These advancements address challenges such as anisotropic data, limited annotations, and the need for robust performance across diverse datasets, ultimately improving diagnostic capabilities and accelerating quantitative image analysis in various fields.