Design of Image Segmentation Algorithm Based on YOLOv3 Network Model

Image segmentation is a fundamental investigation direction in the visual pattern recognition sector, and it plays a major participation in many platforms, such as medical image segmentation. This paper aims to achieve efficient and accurate image segmentation by studying the image segmentation algorithm based on the YOLO network model. The algorithm inherits the advantages of the YOLO network model, including fast and accurate object detection and localization capabilities, and extends them to pixel-level segmentation tasks. This paper presents a left ventricle localization model based on YOLOv3, which is employed to locate different positions within cardiac ventricular images. Initially, a general feature extraction network is utilized to extract common features from cardiac ultrasound images. Subsequently, a deep feature extraction network is employed to obtain the feature matrix for left ventricular ultrasound images. Finally, the feature matrix is input into a detection network to obtain a localization matrix, thereby locating the ventricular region and constraint points. The experimental results demonstrate that the left ventricle localization model designed in this paper, based on YOLOv3, exhibits robust capabilities in region feature extraction and specific positional constraint. The AP50 values for all four target regions exceed 92%, representing a significant improvement compared to commonly used YOLOv1 and YOLOv2 left ventricle localization models.