Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation  

作　　者：Yuchun Li Mengxing Huang Yu Zhang Zhiming Bai 

机构地区：[1]School of Information and Communication Engineering,Hainan University,Haikou,China [2]College of Computer Science and Technology,Hainan University,Haikou,China [3]Urology Department,Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital,Haikou,China [4]School of Information Science and Technology,Hainan Normal University,Haikou,China

出　　处：《Computers, Materials & Continua》2024年第2期1649-1668,共20页计算机、材料和连续体（英文）

基　　金：This work was supported in part by the National Natural Science Foundation of China(Grant#:82260362);in part by the National Key R&D Program of China(Grant#:2021ZD0111000);in part by the Key R&D Project of Hainan Province(Grant#:ZDYF2021SHFZ243);in part by the Major Science and Technology Project of Haikou(Grant#:2020-009).

摘　　要：The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.

关 键 词：Prostate segmentation multi-scale attention 3D Transformer feature fusion MRI 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]