Research On Medical Brain Image Segmentation Algorithm Based On Deep Learning

Medical image segmentation can provide a reliable basis for medical diagnosis and human medical research,so further research on medical image segmentation is of great significance to clinical practice such as surgical design,disease diagnosis and prognosis evaluation.The task of medical image segmentation is to divide the medical image into different regions according to actual needs,and accurately mark the interesting part of the region,and the accuracy of the extraction of these interesting parts determines whether the provided auxiliary diagnosis basis is reliable.Therefore,it is very important to accurately segment the region of interest.This paper mainly studies the algorithms for medical image segmentation under the framework of deep learning,especially considering the problems of low contrast and poor segmentation accuracy in medical brain MRI images,and proposes two medical image segmentation algorithms based on deep neural networks.Experiments on well-established datasets demonstrate the excellent segmentation performance of the proposed network.The research on medical image segmentation in this paper mainly has the following two contributions:(1)In view of the problems of poor segmentation accuracy and low training efficiency of the existing mainstream 3D-UNet segmentation models,a MRADE-Net network segmentation model based on 3D-UNet and cross-reconstruction attention mechanism is proposed.In order to effectively pay attention to the feature information that is more favorable for segmentation in the image,the model proposes a crossreconstruction attention module,which can not only fuse multi-modal data information,but also filter the features to highlight important information.Enhance the performance of the network for image feature expression;in addition,in order to reduce the loss of information during the down-sampling process,a feature difference module is designed,which can transfer the information lost in the down-sampling to the up-sampling without causing information redundancy;In addition,a multi-level deep supervision mechanism is also introduced into the network to speed up the training of the network.The experimental results show that MRADE-Net has better Dice coefficient and higher segmentation accuracy when the parameter quantity is basically the same as that of 3DUnet.(2)A single modal image cannot describe the complete features of the brain image,resulting in the problem of low feature expression ability.In addition,the high-level features in the decoder of the network have inaccurate information representation.Aiming at the above problems,a multi-modality feature reconstruction fusion inverted pyramid network MCRAIP-Net is proposed.The network model adopts the idea of feature pyramid,fuses feature of different levels and sizes,and realizes the segmentation of brain tissue based on the fusion features,and makes full use of context information to extract detailed features of brain images.In addition,a multi-modal cross-reconstruction encoder is designed to fuse the features of different modalities at the same level,thereby improving the segmentation accuracy and network performance.It is worth mentioning that,in this reconstruction encoder,a dual-channel crossreconstruction attention module is proposed to make the multimodal features fused more fully.The experimental results show that the MCRAIP-Net model proposed in this paper has better effects on network training efficiency,segmentation accuracy and network structure similarity.