Prostate Segmentation

Prostate segmentation, the automated identification of prostate boundaries in medical images (MRI, ultrasound, CT), aims to improve the accuracy and efficiency of prostate cancer diagnosis and treatment planning. Current research emphasizes robust generalization across diverse datasets and imaging modalities, employing techniques like continual learning, semi-supervised learning, and attention mechanisms within U-Net and Transformer-based architectures. These advancements address challenges posed by limited annotated data and inter-observer variability, ultimately leading to more reliable and efficient clinical workflows.