Breast ultrasound image tumor segmentation method based on full convolution network

Abstract

The invention belongs to the image processing technology field and particularly relates to a breast ultrasound image tumor segmentation method based on the full convolution network. The method comprises steps that the full convolutional neural network based on cavity convolution is constructed and is for rough segmentation of an ultrasound image to obtain a breast tumor; in the constructed DFCN network, cavity convolution is utilized, so the network is made to maintain the relatively deep-level feature map resolution to ensure that the tumor is well segmented in the presence of a large numberof shaded areas; in addition, the batch normalization technology is utilized in the DFCN network, the network is made to have the higher learning rate, and the training process is accelerated; a dynamic contour PBAC model based on the phase information is utilized to optimize the rough segmentation result to obtain the final fine segmentation result. The experimental result shows that the tumor can be precisely segmented, and the good segmentation result is achieved especially for ultrasound images with blurred boundaries and many shadows.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a breast ultrasound image tumor segmentation method based on a fully convolutional network. Background technique [0002] Ultrasound imaging technology has the advantages of non-invasive, non-radiation, good real-time performance and low cost, and is widely used in breast tumor screening and diagnosis. In clinical applications, tumor contours in breast ultrasound images are usually manually delineated by sonographers, which is very time-consuming. In addition, the results of manual segmentation are highly dependent on the experience of sonographers, and the segmentation results of different observers are not the same. However, due to the characteristics of ultrasound imaging, the automatic segmentation of breast ultrasound images has the following problems: 1) severe speckle noise leads to low contrast and blurred boundaries of ultrasound images; 2) there ar...

AI Technical Summary

Problems solved by technology

However, due to the characteristics of ultrasound imaging, the automatic segmentation of breast ultrasound images has the following problems: 1) severe speckle noise leads to low contrast and blurred boundaries of ultrasound images; 2) there are a large number of shadow areas in the image, which are in gray 3) The shape, size and location of breast tumors are quite different, which puts forward higher requirements for the accuracy and robustness of the segmentation algorithm[1]

However, there are a lot of shadows and speckle noise in breast ultrasound images, making it difficult to obtain satisfactory segmentation results by block-based CNN or U-net

FCN is more suitable for breast ultrasound image segmentation, but the original FCN-8s network has too many parameters, the training process takes a long time, and the segmentation accuracy needs to be improved

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