Research On Automatic Segmentation Algorithm Of Liver And Liver Tumor Based On Deep Fully Convolution Network

With the quick advancement of artificial intelligence,computer-aided determination and treatment are broadly utilized within the medical field.Different medical image segmentation technologies show up and play an imperative part in medical research.In recent years,relevant scholars have carried out unremitting exploration and mining on the feature extraction function of convolutional neural network.The medical image segmentation task has also achieved its own update and optimization by using a variety of deep learning technologies,and achieved better results than the traditional image segmentation method in practice.In any case,due to the complexity of the location,size,shape and texture of human tissue in medical images,blurring contrast,uneven distribution of positive and negative samples and numerous other issues emerge in perpetually,the unadulterated Full Convolutional Network(FCN)structure model show has not accomplished the real clinical practice impact in a few medical image segmentation tasks.In order to get more accurate segmentation results of liver and liver tumor,two deep learning segmentation methods are proposed.The specific methods are as follows:(1)A new method of liver segmentation based on DB-Net structure and feature fusion in the process of down sampling are proposed.Firstly,in order to alleviate the over segmentation problem of liver segmentation,a double branch network of boundary and region is proposed by using the boundary information of liver in CT image.The main function of boundary extraction branch is to constrain and optimize the effect of region segmentation.After that,the experiment is carried out on the liver segmentation dataset of Li TS 2017.The segmentation results show that the boundary of liver segmentation is improved,and the index effect is also improved;In addition,the feature fusion method of Ex Fuse network,which has better classification performance at present,is transferred to the liver segmentation task of abdominal CT image.More semantic information is integrated into the network side supervision to improve the network feature extraction ability and facilitate the network to identify more useful information;The cross entropy loss function is introduced and improved to solve the problem that the background region is much larger than the target region in medical image segmentation,which makes the model training difficult.(2)In this paper,a method of liver tumor segmentation based on multi task learning network is proposed,and a new loss function is used.This method improves the network learning performance by extracting tumor regions and non-tumor regions,and an effective loss function is used,which combines cross entropy loss,JS divergence and mutual exclusion loss to solve the uneven distribution of samples,enhance the complementary feature information in the two tasks,and reduce the shrinkage problem of tumor segmentation.The proposed method not only greatly reduces the number of network parameters and training speed,but also achieves good segmentation results on the Li TS 2017 dataset.These two models are robust to the morphological changes of liver and liver tumor segmentation,and improve the accuracy and performance of tumor segmentation to a certain extent.