Keywords: Automatic Breast Ultrasound, Medical Image Segmentation, Transformer, Self-Attention, Convolutional Neural Networks
TL;DR: Region Aware Transformer for Medical image Segmentation
Abstract: Although Automatic Breast Ultrasound (ABUS) has become an important tool to detect breast cancer, computer-aided diagnosis requires accurate segmentation of tumors on ABUS. In this paper, we propose the Region Aware Transformer Network (RAT-Net) for tumor segmentation on ABUS images. RAT-Net incorporates region prior information of tumors into network design. The specially designed Region Aware Self-Attention Block (RASAB) and Region Aware Transformer Block (RATB) fuse the tumor region information into multi-scale features to obtain accurate segmentation. To the best of our knowledge, it is the first time that tumor region distributions are incorporated into network architectures for ABUS image segmentation. Experimental results on a dataset of 256 subjects (330 ABUS images each) show that RAT-Net outperforms other state-of-the-art methods.
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Paper Type: both
Primary Subject Area: Segmentation
Secondary Subject Area: Detection and Diagnosis
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Code And Data: The code of our method has been open source on the Github: https://github.com/zhenxiner/RAT-Net. Since the study involves the patient's body privacy (breast examination), the data used to support the findings of this study is available from the corresponding author upon reasonable request. And the request also needs to meet the data access requirements of the ethics committee of relevant hospitals.