Parallel processing method and device for fetal ultrasound image intelligent target detection

Abstract

The invention relates to a parallel processing method and device for fetal ultrasound image intelligent target detection, computer equipment and a storage medium. The method comprises the steps: acquiring detection frame data obtained in the fetus ultrasonic scanning process, wherein the detection frame data is an acquired fetal ultrasonic section image; preprocessing the detection frame data to obtain preprocessed detection frame data; performing similarity comparison on the preprocessed detection frame data and the identified reference frame data to obtain a similarity result; and if the similarity result is greater than a preset threshold, taking the identification result of the stored reference frame data as the identification result of the detection frame data to obtain the category and position of the target contained in the detection frame data. By adopting the method, the detection workload of the deep convolutional neural network model can be reduced, and the recognition efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of prenatal ultrasound examination, in particular to a parallel processing method, device, computer equipment and storage medium for intelligent target detection of fetal ultrasound images. Background technique [0002] At present, artificial intelligence has been widely used in the automatic identification of fetal standard slices in ultrasound images, which can accurately help doctors analyze and diagnose fetal growth. In the current intelligent ultrasonic diagnosis process, the acquired ultrasonic image data is firstly preprocessed, such as gray scale processing, histogram equalization processing, and then the trained deep convolutional neural network model is input to the preprocessed image data. Feature extraction and target detection are performed on the dataset, so as to obtain the category and position of the target contained in each fetal ultrasound slice image. [0003] However, the meth...

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Problems solved by technology

[0003] However, the methods in the prior art need to input all the ultrasonic section images obtained during the ultrasound scanning process into the trained deep convolutional neural network model, so as to obtain the target recognition results of the corresponding ultrasonic section images, because the deep convolutional neural network model needs To detect all the detection frame images, the convolution calculation of the model is large, and the detection speed is slow. Therefore, the above method has the problem of low recognition efficiency

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