Fetal Motion
Fetal motion research focuses on accurately measuring, tracking, and correcting fetal movement during prenatal imaging and monitoring. Current research employs advanced image processing techniques, including deep learning models like U-Nets, convolutional neural networks, and diffusion models, to segment fetal structures (e.g., head, brain) from ultrasound and MRI images, improving the accuracy of biometric measurements and fetal brain analysis. These advancements are crucial for enhancing prenatal diagnosis, guiding clinical decision-making, and improving the quality of fetal imaging data, ultimately contributing to safer and more effective pregnancy care.
Papers
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking
Jian Wang, Razieh Faghihpirayesh, Polina Golland, Ali Gholipour
Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
Fangyijie Wang, Guénolé Silvestre, Kathleen M. Curran