Improved vnet Model for 3D Liver CT Image Segmentation
Segmentation of 3D medical images is an important step in radiotherapy planning.In clinical practice,computed tomo-graphy is widely used for 3D medical image segmentation of the liver and liver tumours.Due to the complex edge structure and texture features of the liver,liver segmentation is still a challenging task.To address this problem,an improved vnet model for ac-curate segmentation of 3D liver CT images is proposed.Firstly,the liver CT images are truncated and resampled with HU values to complete the preprocessing of the 3D dataset.Meanwhile,the convolution kernel in the vnet decoder and encoder is replaced with an SG module,which is a combination of depth wise convolution and pointwise convolution,to reduce the number of parame-ters in the network model.Comparative experiments with the vnet model show that the proposed method is generally superior in the evaluation of the liver segmentation dataset,with a Dice coefficient of 94.93％,an improvement of 3.49％over the vnet mod-el,greatly reducing the number of parameters of the model,while the method also shows good robustness and achieves superior segmentation results on the MSD spleen segmentation dataset and COVID-19 dataset.
NETL
NSTL
万方数据
