Fetal Movement Detection and Anatomical Plane Recognition using YOLOv5 Network in Ultrasound Scans

Abstract

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 deep-SORT 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.

Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models

“…The accuracy of head circumference biometry from ultrasound pictures is increased by the network design shown in this paper, which blends attention processes with segmentation models. An approach for detecting fetal movement and recognising anatomical planes is presented by Dandıl et al (2021), which makes use of the YOLOv5 network. In order to aid in thorough evaluations of fetal health, the authors employ this network to recognise anatomical features and track fetal movement in ultrasound images.…”

Deep-learning-based sampling position selection on color doppler sonography images during renal artery ultrasound scanning

“…32 Besides, Faster R-CNN and YOLOv5 models were also applied in detecting breast nodules and recognizing fetal anatomical plane. 33,34 In 2022 Dadoun et al tried the NLP-based model, DETR, to locate the focal liver lesions in abdominal ultrasound image, and the performance of DETR preceded the Faster R-CNN. 35 It could be seen that most of these ultrasonic imaging studies recognized the interest objects based on the Faster R-CNN or YOLO series models, and didn't assess the differences of predictive validity among various types of object detection models to select the most suitable model.…”