3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ankara, Turkey, 11 - 13 June 2021, no.196, pp.54
Analyzing medical images and videos with computer-aided algorithms provides important benefits in the diagnosis and treatment of
diseases. Especially in recent years, the increasing developments in deep learning algorithms have provided continuous improvement
in subjects such as speed, performance and hardware need in the processing of medical data. Examination of medical data, which may
require advanced expertise, using deep learning algorithms has begun to be widely used as a secondary tool in the decision-making
process of physicians. Tracking the movements of the fetus and recognizing its planes in ultrasound (US) videos is an important
parameter in evaluating the health of the baby. In this study, a YOLOv5 deep learning network based method is proposed to identify
fetal anatomical planes from fetal ultrasound and to detect their movements. First of all, a dataset of videos containing 16-20 weeks of
fetal movements is created in the study. In the next step, the fetal head, arm, heart and body are identified and tracking using the deepSORT algorithm on the labeled data. In the experimental studies conducted on ultrasound videos within the scope of the study, using
the YOLOv5 algorithm, head, body, heart and arm are recognized with 95.04%, 94.42%, 88.31% and 83.23% F1-score, respectively.
In addition, ultrasonic video movements of the head, heart and body of the fetus are followed and the trajectories and patterns of the
movements are extracted. Thus, the detection of fetal movements from the movement patterns transformed into a two-dimensional
plane is achieved.
Keywords: Fetus, Ultrasound video, Deep learning, Anatomical plane recognition, Fetal movement detection, YOLOv5, Deep-SORT
algorithm, Object tracking.