Tissue Segmentation in Nasopharyngeal CT Images Using Two-Stage Learning

Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis.However,it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure.In this paper,we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net.In the proposed methodology,the network consists of two segmentation modules.The first module performs rough segmentation and the second module performs accurate segmentation.Considering the training time and the limitation of computing resources,the structure of the second module is simpler and the number of network layers is less.In addition,our segmentation module is based on U-Net and incorporates a skip structure,which can make full use of the original features of the data and avoid feature loss.We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University.The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method,and can be easily generalized across different tissue types in various organs.

ICA-Unet:An improved U-net network for brown adipose tissue segmentation

Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and secretory.Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases.Additionally,the activity of BAT presents certain differences between different ages and genders.Clinically,BAT segmentation based on PET/CT data is a reliable method for brown fat research.However,most of the current BAT segmentation methods rely on the experience of doctors.In this paper,an improved U-net network,ICA-Unet,is proposed to achieve automatic and precise segmentation of BAT.First,the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional(Do-Conv)layer.Second,the channel attention block is introduced between the double-layer convolution.Finally,the image information entropy(IIE)block is added in the skip connections to strengthen the edge features.Furthermore,the performance of this method is evaluated on the dataset of PET/CT images from 368 patients.The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts.The average DICE coeffcient(DSC)is 0.9057,and the average Hausdorff distance is 7.2810.Experimental results suggest that the method proposed in this paper can achieve effcient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.