Manual Segmentation

Manual segmentation, the gold standard for delineating structures in medical images, is time-consuming and prone to inter-observer variability. Current research focuses on developing automated segmentation methods using deep learning architectures like U-Nets and convolutional neural networks, often incorporating techniques like data augmentation with synthetic images and interactive segmentation to improve accuracy and efficiency. These advancements aim to reduce the reliance on manual annotation, enabling larger-scale studies and improving the reproducibility and objectivity of analyses across various medical imaging applications, including oncology, cardiology, and ophthalmology. The ultimate goal is to create robust, accurate, and clinically applicable automated segmentation tools.