Improved U-net MALF model for lesion segmentation in breast ultrasound images

Abstract

Breast cancer poses a great threat on women health due to its high malignant rate. In China, ultrasound screening is the commonly-used method for breast cancer diagnosis, and the localization and segmentation of the lesions in ultrasound images are helpful for breast cancer detection. In this paper, an Improved U-net based on Mixed Attention Loss Function (Improved U-net MALF) model was proposed and employed to segment the breast tumors in ultrasound images. Firstly, based on the attention U-net network framework, the residual convolution module and extended residual convolution module were used to replace the convolution module of the coding path, so as to extract more detailed features of ultrasound breast tumor without increasing the computational cost. Secondly, on the basis of the traditional cross entropy loss function, four attention loss functions were integrated. The proportion of the four attention function loss values in the total loss value was measured by the texture consistency index of the feature map to further highlight the tumor target. The combined application of residual block and hybrid attention loss function improves the segmentation accuracy of breast tumor greatly. The results show that our model presented excellent segmentation results as compared to other Deep Networks when tested breast ultrasound images. A variety of quantitative indicators provided performances above 80 % including accuracy, specificity and sensitivity. It illustrates that the proposed Improved U-net MALF model can accurately segment breast lesions and thus has a good prospect for clinical diagnosis.