3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework

BMC Medical Imaging

Guang Yang¹  Weiji Yang²  Jianming Ye³  Xiaomei Xu⁴  Xiaobo Lai⁴  Weiwei Jiang⁴  Xi Guan⁴ 

[1] Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK;National Heart and Lung Institute, Imperial College London, SW7 2AZ, London, UK;College of Life Science, Zhejiang Chinese Medical University, 310053, Hangzhou, China;First Affiliated Hospital, Gannan Medical University, 341000, Ganzhou, China;School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, 310053, Hangzhou, China;

关键词: Brain tumor;    Magnetic resonance imaging;    VNet;    Automatic segmentation;    Deep learning;   

DOI  :  10.1186/s12880-021-00728-8

来源: Springer

【 摘 要 】

BackgroundGlioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. With the continuous development of deep learning, researchers have designed many automatic segmentation algorithms; however, there are still some problems: (1) The research of segmentation algorithm mostly stays on the 2D plane, this will reduce the accuracy of 3D image feature extraction to a certain extent. (2) MRI images have gray-scale offset fields that make it difficult to divide the contours accurately.MethodsTo meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise.ResultsWe used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor, tumor core and enhanced tumor are 0.68, 0.85 and 0.70, respectively.ConclusionAlthough MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients.

【 授权许可】

CC BY