A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm

The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospective measurement of fetal thalamus diameter (FTD). Unfortunately, FTD is not measured in routine 2D ultrasound (2D-US) screenings of fetuses. Manual measurement of FTD is a laborious, difficult, and error-prone process because the thalamus lacks well-defined boundaries in 2D-US images of the fetal brain as it has a similar echogenicity to the surrounding brain tissue. Traditional methods based on statistical shape models (SSMs) perform poorly in measuring FTD due to the noisy textures and fuzzy edges of the fetal thalamus in 2D-US images of the fetal brain. To overcome these difficulties, we propose a deep learning-based automatic FTD measurement algorithm, FTDNet. FTDNet measures FTD by learning to directly detect the measurement landmarks through supervised learning. The algorithm first detects the region of the brain that contains the thalamus structure, and then focuses on processing that region for FTD landmark detection. Our FTD dataset, developed through a consensus between two ultrasonographers, contains 1,111 pairs of landmark coordinates for measuring FTD and verified bounding boxes surrounding the fetal thalamus. To assess FTDNet's measurement consistency compared to the ground truth, we used the intraclass correlation coefficient (ICC). FTDNet achieved an ICC score of 0.734, significantly outperforming the prior SSM method and other baseline comparison methods. Our findings are an important step forward in understanding the maternal factors which influence fetal brain development.