Obtaining fetal biological indicators from ultrasound images plays a significant role in diagnosing fetal abnormality. However, manual measurement by physicians is not only subjective, but also leads to inefficiency under repeated operations. To solve the above problems, we propose an improved ultrasound image segmentation algorithm based on DenseASPP model to assist in the measurement of fetal indicators. According to the atrous convolution and structure of Atrous Spatial Pyramid Pooling, the authors firstly extract the pre feature maps of the original image by the ordinary convolution, then the output of each atrous layer is concatenated with the input feature map and all the outputs from lower layers, and the concatenated feature map is fed into the following layer. Finally, all the features are merged to obtain the relevant features through the Attention mechanism, and sigmoid function is used to obtain the segmentation results. We evaluate the method using ultrasound images of fetal head and hip diameters, head circumference, and abdominal circumference as data sets. The experimental results show that this method is superior to other advanced segmentation methods and has better performance.
