Automatic breast mass segmentation in ultrasound images with U-Net and resolution enhancement blocks

Abstract

This research proposes a deep learning method for breast mass segmentation in 2D ultrasound images. The low quality of ultrasound images and indistinct boundaries of masses cause difficulties in the automatic segmentation of breast masses. To solve this problem, we introduce an autoencoder-based model that is equipped with resolution enhancement blocks. The proposed model is composed of U-Net, along with residual blocks and attention blocks. Its objective is to increase the accuracy of breast mass segmentation by enhancing the feature maps and paying more attention to the region of interest, and preventing the loss of features in the encoder part of U-Net, this helps to rebuild an image with better quality in the decoder section. The proposed model achieved an accuracy of 98.84% and a Dice score of 84.72% for image containing benign tumor and 82.14% for image containing malignant tumor. For the IoU metric, the model obtained scores of 85.95% and 82.21% for images containing benign and malignant tumors, respectively. These results showed an improvement in segmentation accuracy compared to the performance of previous models based on U-Net. This highlights the usefulness and advantage of using U-Net and resolution enhancement blocks for breast mass segmentation11The code for the implemented model can be found in the article's GitHub..