CT Image Tumor Segmentation Model Combined with Dense Feature Mapping

Different from the conventional segmentation object,the proportion of tumor tissues in medical images is small and the anatomical structure is similar to that of human narrow tissues.Furthermore,the un-clear difference between tumors leads to the poor segmentation performance compared with the expected value.Therefore,in order to enhance the effectiveness of tumor feature transfer,a tumor segmentation model combined with high/low dimensional dense feature mapping is proposed.Firstly,the feature three-dimen-sional(3D)mapping is used to optimize the network parameters,the CT images are aggregated into a 3D sequence structure for hard threshold 3D transformation,for establishing the feature connections and reduc-ing irreversible initial feature loss.Then,a dense convolutional network is built and ReLU is replaced by SELU activation function,in order to activate the network and introduce negative parameters to avoid“dead features”.In addition,a layer of maximum pooling layer is also added after each dense block to reduce the capital consumption of time and space.Finally,via fusing the channel and spatial features,the feature emersion method is adopted for feature reconstruction,which improves the ability of feature expression obvi-ously.The classic segmentation methods such as U-Net are compared under the TensorFlow environment,and ablation experiments are performed.Experiments on CT image dataset provided by Shandong Provincal Qianfoshan Hospital show that proposed model effectively improves the accuracy of tumor segmentation.Compared with the existing classical models,proposed model achieves better performance in terms of aver-age pixel accuracy and average intersection ratio.