Research On Brain Tumor Segmentation For Multimodal MRI Images Based On 3DU-Net

Brain tumors are the most common primary tumors,and MRI imaging is one of the common imaging techniques.Using image segmentation algorithms to accurately segment brain tumor images,doctors can obtain multiple information such as the shape,size,and location of tumors,and then conduct quantitative analysis and tracking comparison of tumors,including location and measurement of lesions,quantitative analysis and diagnosis of tissue volume,research on internal anatomical structures,preoperative analysis,and formulation of surgical plans,thereby assisting in surgical navigation,formulating radiotherapy plans,and locating radiation targets,the establishment of disease tracking files,pathological research and analysis,and the establishment of tumor models and maps play a crucial role.In recent years,medical image segmentation methods based on deep learning have received widespread attention and application.Based on this,this paper proposed a series of segmentation model based on 3DU-Net for brain tumor MRI images in terms of image data processing and feature extraction.The main work of this paper can be concluded as follows:(1)A multimodal magnetic resonance brain tumor image segmentation model based on 3DU-Net with nonlocal structure is proposed.This model preprocesses brain tumor MRI images by introducing a modal weight module,extracts nonlocal feature information by constructing a nonlocal structural module during the sampling process of the 3DU-Net,and uses weighted features containing nonlocal information to replace the original network features for model training.Multiple datasets have been experimented on,and the results of the experiments indicate that our method can further improve the segmentation accuracy of the 3DU-Net model in brain tumor image segmentation task.(2)An improved 3DU-Net multimodal magnetic resonance image segmentation method for brain tumors based on attention-guided is proposed.In order to further improve the utilization of structural information in 3DU-Net network,the traditional image processing technique filter is combined with 3DU-Net.The guided filter is exploited as a structure sensitive expanding path to transfer structural information from previous feature maps,and an attention block is introduced to exclude the noise and reduce the negative influence of background further.The attention-guided filter module is used to replace the skip-connection layer and thus migrate the structural information extracted from the shallow layer to the deep layer.The experiments show that the method further improves the performance of 3DU-Net in the brain tumor MRI image segmentation tasks.