Abstract

Info

Author(s):: KANG Shuai1; XI Zhenghao1; HUANG Chen2; FU Zhongmao2; LIU Xiang1; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China

Keywords:: Keywords: rectal cancer segmentation T staging attention mechanism multi-modality fusion

Abstract:: Abstract: The segmentation and T staging of rectal tumor in MRI images are critical in preoperative diagnosis and treatment planning. A multi-task learning model, DPU-Net, is proposed to achieve both accurate tumor segmentation and T staging. In the segmentation path, considering the complex structure of rectal cancer MRI images, attention mechanism and multi-scale features are used to enhance the models focus on tumor and the edge feature extraction ability, thereby improving segmentation performance. In the classification path, medical treatment texts are introduced to make full use of medical data and a multi-modality fusion model based on dynamic weight which combines image features and text features is established for T staging. The experimental results show that the proposed model achieves a Dice similarity coefficient of 82.88%, which is 17.96% higher than U-Net, and that the staging accuracy is 76.24%, which is 9.43% higher as compared with Dense-Net. The proposed method is feasible for auxiliary diagnosis.