Deep Learning-Based Segmentation of Fetal Anatomical Structures in the First Trimester.

OBJECTIVES

To develop and evaluate an artificial intelligence (AI) system that automatically identifies and classifies the fetal structures in the first trimester.

METHOD: Over 20,000 ultrasound images from first-trimester fetuses were prospectively collected from four university hospitals in the Republic of Korea. Images were annotated according to segmentation-specific structures by anatomical regions, including the head, neck, thorax, abdomen, extremities, and spine, based on standardized guidelines. The YOLACT model, which enabels real-time instance segmentation, was used to detect and segment fetal structures. The dataset was randomly divided into a training set (95%) and a test set (5%). Model performance was evaluated using detection accuracy, mean average precision (mAP), and frames per second (FPS).

RESULTS

The YOLACT model achieved an overall anatomical detection accuracy of 98.4%. High segmentation performance (F1-score > 0.950) was observed for well-defined structures such as the cranium, heart, and abdominal circumference. Structures like the nasal bone and extremities had relatively lower recall. The model's mAP at IoU 0.5 was 0.622, and real-time processing was confirmed with a speed of 25.4 FPS.

CONCLUSIONS

The YOLACT-based AI model demonstrated accurate and efficient segmentation of fetal structures in the first trimester, supporting its potential for real-time clinical application in early anomaly screening.