Placenta Segmentation

Placenta segmentation, the automated identification of the placenta's boundaries in medical images, aims to improve prenatal care by enabling faster, more accurate measurements and analyses. Current research heavily utilizes deep learning, employing architectures like U-Net and SegNeXt, often combined with ensemble learning or multi-modal fusion strategies (e.g., integrating B-mode and power Doppler ultrasound data) to overcome challenges posed by image noise and variability. These advancements offer the potential for improved prediction of pregnancy complications, facilitating earlier interventions and better perinatal outcomes, while also reducing the time and cost associated with manual annotation.