WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound

Authors:
raw_author_name: Donya Khaledyan
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Rochester, 500 Joseph C. Wilson Blvd., Rochester, New York, 14627, UNITED STATES
raw_author_name: Thomas J. Marini
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, New York, 14642, UNITED STATES
raw_author_name: Avice M. O’Connell
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 500 Red Creek Dr, UR Medicine Breast Imaging, Rochester, New York, 14643, UNITED STATES
raw_author_name: Steven Meng
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, Rochester, 14642, UNITED STATES
raw_author_name: Jonah Kan
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 500 Joseph C. Wilson Blvd., Rochester, Rochester, 14642, UNITED STATES
raw_author_name: Galen Brennan
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 500 Joseph C. Wilson Blvd., Rochester, Rochester, 14642, UNITED STATES
raw_author_name: Yu Zhao
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 500 Joseph C. Wilson Blvd., Rochester, Rochester, 14642, UNITED STATES
raw_author_name: Timothy M. Baran
raw_affiliation_string: Department of Imaging Sciences, University of Rochester Medical Center, 500 Joseph C. Wilson Blvd., Rochester, Rochester, 14642, UNITED STATES
raw_author_name: Kevin J Parker
raw_affiliation_string: Electrical & Computer Engineering, University of Rochester, P.O. Box 270231, 724 Computer Studies Building, Rochester, NY 14627-0231, USA, Rochester, New York, 14627-0231, UNITED STATES

Abstract: 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. 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, 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 and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public 'Breast Ultrasound Images' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. 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. Moreover, our model significantly outperformed other models in McNemar's test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.