Abstract: The diagnosis of developmental dysplasia of the hip (DDH) by Graf method mainly depends on the seven key structures, involving chondro-osseous border, femoral head, cartilagineous roof, synovial fold, labrum, bony roof and joint capsule. However, it is difficult for junior doctors to identify these structures. Therefore, a network model based on Deeplabv3+ for the segmentation of these seven structures is proposed in this paper. Firstly, 106 images were manually labeled and preprocessed, and then they were input into Deeplabv3+ and U-net network models respectively. Finally, the segmentation performances were compared. Compared with U-Net network which is commonly used and well-behaved in DDH segmentation, the image prediction of Deeplabv3+ network contain more structures and clearer boundary, and the main evaluation indices of segmentation, such as the average values of dice similarity coefficient, Hausdorff distance, average Hausdoff distance, also showed a better performance. The Deeplabv3+ network is first used to achieve segmentation of seven structures in DDH ultrasound images, which is of great significance for angle measurement and classification diagnosis.