WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep
  Imaging Ultrasound
Donya Khaledyan Thomas J. Marini Avice OConnell Steven Meng Jonah Kan Galen Brennan Yu Zhao Timothy M. Baran Kevin J. Parker
Objective. Limited access to breast cancer diagnosis globally leads to
delayed treatment. Ultrasound, an effective yet underutilized method, requires
specialized training for sonographers, which hinders its widespread use.
Approach. Volume sweep imaging (VSI) is an innovative approach that enables
untrained operators to capture high-quality ultrasound images. Combined with
deep learning, like convolutional neural networks (CNNs), it can potentially
transform breast cancer diagnosis, enhancing accuracy, saving time and costs,
and improving patient outcomes. The widely used UNet architecture, known for
medical image segmentation, has limitations, such as vanishing gradients and a
lack of multi-scale feature extraction and selective region attention. In this
study, we present a novel segmentation model known as Wavelet_Attention_UNet
(WATUNet). In this model, we incorporate wavelet gates (WGs) and attention
gates (AGs) between the encoder and decoder instead of a simple connection to
overcome the limitations mentioned, thereby improving model performance. Main
results. Two datasets are utilized for the analysis. The public "Breast
Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818
images. Both datasets contained segmented lesions categorized into three types:
no mass, benign mass, and malignant mass. Our segmentation results show
superior performance compared to other deep networks. The proposed algorithm
attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset
and scored 0.93 and 0.94 on the public dataset, respectively.