Membrane Segmentation

Membrane segmentation, the automated identification and delineation of cell membranes in images, is crucial for various applications across biology and medicine. Current research heavily utilizes deep learning, particularly employing U-Net architectures and instance segmentation methods, often enhanced by techniques like unsupervised domain adaptation and uncertainty quantification to improve accuracy and robustness across diverse image modalities and datasets. These advancements are significantly impacting fields like pathology (e.g., cancer biomarker quantification), neurobiology (e.g., connectomics), and ophthalmology (e.g., AMD diagnosis), enabling faster, more accurate, and objective analysis of complex biological structures. The incorporation of anatomical priors and the estimation of segmentation uncertainty are emerging trends aimed at improving the reliability and clinical applicability of these methods.