Mitochondrion Segmentation

Mitochondrion segmentation, the automated identification and delineation of mitochondria in microscopy images, is crucial for understanding their role in health and disease. Current research focuses on developing robust and adaptable segmentation algorithms, employing deep learning architectures like U-Nets, transformers, and generative adversarial networks (GANs), often incorporating techniques like domain adaptation and self-supervised learning to handle variations in imaging modalities and data scarcity. These advancements enable high-throughput analysis of mitochondrial morphology and spatial relationships, facilitating investigations into diverse biological processes and potentially accelerating the discovery of disease biomarkers and therapeutic targets.