3D Deep Attentive U-Net with Transformer for Breast Tumor Segmentation from Automated Breast Volume Scanner

Abstract

Breast cancer has become the primary factor threatening women's health. Automated breast volume scanner (ABVS) is applied for automatic scanning which is meaningful for the rapid and accurate detection of breast tumor. However, accurate segmentation of tumor regions is a huge challenge for clinicians from the ABVS images since it has the large image size and low data quality. Therefore, we propose a novel 3D deep convolutional neural network for automatic breast tumor segmentation from ABVS data. The structure based on 3D U-Net is designed with attention mechanism and transformer layers to optimize the extracted image features. In addition, we integrate the atrous spatial pyramid pooling block and the deep supervision for further performance improvement. The experimental results demonstrate that our model has achieved dice coefficient of 76.36% for 3D segmentation of breast tumor via our self-collected data.